De-An Huang, Minghuang Ma, Wei-Chiu Ma, Kris M. Kitani

Abstract: Our aim is to show how state-of-the-art computer vision techniques can be used to advance prehensile analysis (i.e., understanding the functionality of human hands). Prehensile analysis is a broad field of multi-disciplinary interest, where researchers painstakingly manually analyze hours of hand-object interaction videos to understand the mechanics of hand manipulation. In this work, we present promising empirical results indicating that wearable cameras and unsupervised clustering techniques can be used to automatically discover common modes of human hand use. In particular, we use a first-person point-of-view camera to record common manipulation tasks and leverage its strengths for reliably observing human hand use. To learn a diverse set of hand-object interactions, we propose a fast online clustering algorithm based on the Determinantal Point Process (DPP). Furthermore, we develop a hierarchical extension to the DPP clustering algorithm and show that it can be used to discover appearance-based grasp taxonomies. Using a purely data-driven approach, our proposed algorithm is able to obtain hand grasp taxonomies that roughly correspond to the classic Cutkosky grasp taxonomy. We validate our approach on over 10 hours of first-person point-of-view videos in both choreographed and real-life scenarios.

@InProceedings{Huang_2015_CVPR,author = {Huang, De-An and Ma, Minghuang and Ma, Wei-Chiu and Kitani, Kris M.},title = {How Do We Use Our Hands? Discovering a Diverse Set of Common Grasps},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images. This type of blur is common during photo taking. Although it is not strong, the slight edge blurriness contains informative clues related to depth. We found existing blur descriptors based on local information cannot distinguish this type of small blur reliably from unblurred structures. We propose a simple yet effective blur feature via sparse representation and image decomposition. It directly establishes correspondence between sparse edge representation and blur strength estimation. Extensive experiments manifest the generality and robustness of this feature.

@InProceedings{Shi_2015_CVPR,author = {Shi, Jianping and Xu, Li and Jia, Jiaya},title = {Just Noticeable Defocus Blur Detection and Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Junho Yim, Heechul Jung, ByungIn Yoo, Changkyu Choi, Dusik Park, Junmo Kim

Abstract: Face recognition under viewpoint and illumination changes is a difficult problem, so many researchers have tried to solve this problem by producing the pose- and illumination- invariant feature. Zhu et al. [26] changed all arbitrary pose and illumination images to the frontal view image to use for the invariant feature. In this scheme, preserving identity while rotating pose image is a crucial issue. This paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination image while preserving identity. The target pose can be controlled by the user's intention. This novel type of multi-task model significantly improves identity preservation over the single task model. By using all the synthesized controlled pose images, called Controlled Pose Image (CPI), for the pose- illumination- invariant feature and voting among the multiple face recognition results, we clearly outperform the state-of-the-art algorithms by more than 4~6% on the MultiPIE dataset.

@InProceedings{Yim_2015_CVPR,author = {Yim, Junho and Jung, Heechul and Yoo, ByungIn and Choi, Changkyu and Park, Dusik and Kim, Junmo},title = {Rotating Your Face Using Multi-Task Deep Neural Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic

Abstract: Successful visual object recognition methods typically rely on training datasets containing lots of richly annotated images. Annotating object bounding boxes is both expensive and subjective. We describe a weakly supervised convolutional neural network (CNN) for object classification that relies only on image-level labels, yet can learn from cluttered scenes containing multiple objects. We quantify its object classification and object location prediction performance on the Pascal VOC 2012 (20 object classes) and the much larger Microsoft COCO (80 object classes) datasets. We find that the network (i) outputs accurate image-level labels, (ii) predicts approximate locations (but not extents) of objects, and (iii) performs similar or better compared to its fully-supervised counterparts using object bounding box annotation for training.

@InProceedings{Oquab_2015_CVPR,author = {Oquab, Maxime and Bottou, Leon and Laptev, Ivan and Sivic, Josef},title = {Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Global Refinement of Random Forest

Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun

Abstract: Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniques, global refinement and global pruning, to improve a pre-trained random forest. The proposed global refinement jointly relearns the leaf nodes of all trees under a global objective function so that the complementary information between multiple trees is well exploited. In this way, the fitting power of the forest is significantly enhanced. The global pruning is developed to reduce  the model size as well as the over-fitting risk. The refined model has better performance and smaller storage cost, as verified in extensive experiments.

@InProceedings{Ren_2015_CVPR,author = {Ren, Shaoqing and Cao, Xudong and Wei, Yichen and Sun, Jian},title = {Global Refinement of Random Forest},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hang Yang, Ming Zhu, Yan Niu, Yujing Guan, Zhongbo Zhang

Abstract:
The following message is relayed from an update made on IEEE Xplore.

Notice of Violation of IEEE Publication Principles

"Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution"
by Hang Yang, Ming Zhu, Yan Niu, Yujing Guan, and Zhongbo Zhang
in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, pp. 705-713

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper copied portions of text from the papers cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper titles) and without permission.

"Group-Based Sparse Representation for Image Restoration"
by Jian Zhang, Debin Zhao, and Wen Gao
in the IEEE Transactions on Image Processing, Vol 23, No 8, August 2014, pp. 3336-3351

"Dual-domain Image Denoising"
by Claude Knaus, Matthias Zwicker
in the Proceedings of the IEEE International Conference on Image Processing, (ICIP), September 2013, pp. 440-444

"A Machine Learning Approach for Non-blind Image Deconvolution"
by Christian Schuler, Harold Christopher Burger, Stefan Harmeling, and Bernhard Scholkopf
in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2013, pp. 1067-1074

Image deconvolution continues to be an active research topic of recovering a sharp image, given a blurry one generated by a convolution. One of the most challenging problems in image deconvolution is how to preserve the fine scale texture structures while removing blur and noise. Various methods have been implemented in both spatial and transform domains, such as gradient based methods, nonlocal self-similarity methods, sparsity based methods.  However, each domain has its advantages and shortcomings, which can be complemented by each other. In this work we propose a new approach for efficient image deconvolution based on dual domain filters. In the deblurring process, we offer a hybrid method that a novel rolling guidance filter is used to ensure proper texture/structure separation, and then in the transform domain, we use the short-time Fourier transform to recover the textures while removing noise with energy shrinkage. Our hybrid algorithm that is surprisingly easy to implement, and experimental results clearly show that the proposed algorithm outperforms many state-of-the-art deconvolution algorithms in terms of both quantitative measure and visual perception quality.

@InProceedings{Yang_2015_CVPR,author = {Yang, Hang and Zhu, Ming and Niu, Yan and Guan, Yujing and Zhang, Zhongbo},title = {Notice of Violation of IEEE Publication Principles: Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xuan Dong, Boyan Bonev, Yu Zhu, Alan L. Yuille

Abstract: We study the problem of temporally consistent video post-processing. Previous post-processing algorithms usually either fail to keep high fidelity or fail to keep temporal consistency of output videos. In this paper, we observe experimentally that many image/video enhancement algorithms enforce a spatially consistent prior on the enhancement. More precisely, within a local region, the enhancement is consistent, i.e., pixels with the same RGB values will get the same enhancement values. Using this prior, we segment each frame into several regions and temporally-spatially adjust the enhancement of regions of different frames, taking into account fidelity, temporal consistency and spatial consistency. User study, objective measurement and visual quality comparisons are conducted. The experimental results demonstrate that our output videos can keep high fidelity and temporal consistency at the same time.

@InProceedings{Dong_2015_CVPR,author = {Dong, Xuan and Bonev, Boyan and Zhu, Yu and Yuille, Alan L.},title = {Region-Based Temporally Consistent Video Post-Processing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Xinge You, Qinglong Liu, Dong Yue, Ruimin Hu, Baowen Xu

Abstract: Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD^2L) approach for SR person re-identification. For the given training image set which consists of HR gallery and LR probe images, we aim to convert the features of LR images into discriminating HR features. Specifically, our approach learns a pair of HR and LR dictionaries and a mapping from the features of HR gallery images and LR probe images. To ensure that the converted features using the learned dictionaries and mapping have favorable discriminative capability, we design a discriminant term which requires the converted HR features of LR probe images should be close to the features of HR gallery images from the same person, but far away from the features of HR gallery images from different persons. In addition, we apply low-rank regularization in dictionary learning procedure such that the learned dictionaries can well characterize intrinsic feature space of HR and LR images. Experimental results on public datasets demonstrate the effectiveness of SLD^2L.

@InProceedings{Jing_2015_CVPR,author = {Jing, Xiao-Yuan and Zhu, Xiaoke and Wu, Fei and You, Xinge and Liu, Qinglong and Yue, Dong and Hu, Ruimin and Xu, Baowen},title = {Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yi-Hsuan Tsai, Onur C. Hamsici, Ming-Hsuan Yang

Abstract: Learning models for object detection is a challenging problem due to the large intra-class variability of objects in appearance, viewpoints, and rigidity. We address this variability by a novel feature pooling method that is adaptive to segmented regions. The proposed detection algorithm automatically discovers a diverse set of exemplars and their distinctive parts which are used to encode the region structure by the proposed feature pooling method. Based on each exemplar and its parts, a regression model is learned with samples selected by a coarse region matching scheme. The proposed algorithm performs favorably on the PASCAL VOC 2007 dataset against existing algorithms. We demonstrate the benefits of our feature pooling method when compared to conventional spatial pyramid pooling features. We also show that object information can be transferred through exemplars for detected objects.

@InProceedings{Tsai_2015_CVPR,author = {Tsai, Yi-Hsuan and Hamsici, Onur C. and Yang, Ming-Hsuan},title = {Adaptive Region Pooling for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mohammad Rastegari, Hannaneh Hajishirzi, Ali Farhadi

Abstract: In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a ranking such that positive (top rank) instances are {\it highly and consistently similar} to each other and dissimilar to negative instances. Our approach takes advantage of a discriminative notion of pairwise similarity coupled with a structural cue in the form of a consistency metric that measures the quality of each similarity. We learn a similarity function for every pair of instances in positive bags by how similarly they differ from instances in negative bags, the only certain labels in MIL.  Our experiments demonstrate that our method  consistently outperforms state-of-the-art MIL methods both at bag-level and instance-level predictions in standard benchmarks, image category recognition, and text categorization datasets.

@InProceedings{Rastegari_2015_CVPR,author = {Rastegari, Mohammad and Hajishirzi, Hannaneh and Farhadi, Ali},title = {Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zhibin Hong, Zhe Chen, Chaohui Wang, Xue Mei, Danil Prokhorov, Dacheng Tao

Abstract: Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking. Here, we adopt cognitive psychology principles to design a flexible representation that can adapt to changes in object appearance during tracking. Inspired by the well-known Atkinson-Shiffrin Memory Model, we propose MUlti-Store Tracker (MUSTer), a dual-component approach consisting of short- and long-term memory stores to process target appearance memories. A powerful and efficient Integrated Correlation Filter (ICF) is employed in the short-term store for short-term tracking. The integrated long-term component, which is based on keypoint matching-tracking and RANSAC estimation, can interact with the long-term memory and provide additional information for output control. MUSTer was extensively evaluated on the CVPR2013 Online Object Tracking Benchmark (OOTB) and ALOV++ datasets. The experimental results demonstrated the superior performance of MUSTer in comparison with other state-of-art trackers.

@InProceedings{Hong_2015_CVPR,author = {Hong, Zhibin and Chen, Zhe and Wang, Chaohui and Mei, Xue and Prokhorov, Danil and Tao, Dacheng},title = {MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Finding Action Tubes

Abstract: We address the problem of action detection in videos. Driven by the latest progress in object detection from 2D images, we build action models using rich feature hierarchies derived from shape and kinematic cues. We incorporate appearance and motion in two ways. First, starting from image region proposals we select those that are motion salient and thus are more likely to contain the action. This leads to a significant reduction in the number of regions being processed and allows for faster computations. Second, we extract spatio-temporal feature representations to build strong classifiers using Convolutional Neural Networks. We link our predictions to produce detections consistent in time, which we call action tubes. We show that our approach outperforms other techniques in the task of action detection.

@InProceedings{Gkioxari_2015_CVPR,author = {Gkioxari, Georgia and Malik, Jitendra},title = {Finding Action Tubes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce

Abstract: In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

@InProceedings{Sun_2015_CVPR,author = {Sun, Jian and Cao, Wenfei and Xu, Zongben and Ponce, Jean},title = {Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yao Xiao, Cewu Lu, Efstratios Tsougenis, Yongyi Lu, Chi-Keung Tang

Abstract: Distance metric plays a key role in grouping superpixels to produce object proposals for object detection. We observe that existing distance metrics work primarily for low complexity cases. In this paper, we develop a novel distance metric for grouping two superpixels in high-complexity scenarios. Combining them, a complexity-adaptive distance measure is produced that achieves improved grouping in different levels of complexity. Our extensive experimentation shows that our method can achieve good results in the PASCAL VOC 2012 dataset surpassing the latest state-of-the-art methods.

@InProceedings{Xiao_2015_CVPR,author = {Xiao, Yao and Lu, Cewu and Tsougenis, Efstratios and Lu, Yongyi and Tang, Chi-Keung},title = {Complexity-Adaptive Distance Metric for Object Proposals Generation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper presents a generic method for transforming MRFs for the marginal inference problem. Its major application is to downsize MRFs to speed up the computation. Unlike the MAP inference, there are only classical algorithms for the marginal inference problem such as BP etc. that require large computational cost. Although downsizing MRFs should directly reduce the computational cost, there is no systematic way of doing this, since it is unclear how to obtain the MRF energy for the downsized MRFs and also how to translate the estimates of their marginal distributions to those of the original MRFs. The proposed method resolves these issues by a novel probabilistic formulation of MRF transformation. The key idea is to represent the joint distribution of an MRF with that of the transformed one, in which the variables of the latter are treated as latent variables. We also show that the proposed method can be applied to discretization of variable space of continuous MRFs and can be used with Markov chain Monte Carlo methods. The experimental results demonstrate the effectiveness of the proposed method.

@InProceedings{Saito_2015_CVPR,author = {Saito, Masaki and Okatani, Takayuki},title = {Transformation of Markov Random Fields for Marginal Distribution Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xiangyu Zhu, Zhen Lei, Junjie Yan, Dong Yi, Stan Z. Li

Abstract: Pose and expression normalization is a crucial step to recover the canonical view of faces under arbitrary conditions, so as to improve the face recognition performance. An ideal normalization method is desired to be automatic, database independent and high-fidelity, where the face appearance should be preserved with little artifact and information loss. However, most normalization methods fail to satisfy one or more of the goals. In this paper, we propose a High-fidelity Pose and Expression Normalization (HPEN) method with 3D Morphable Model (3DMM) which can automatically generate a natural face image in frontal pose and neutral expression. Specifically, we firstly make a landmark marching assumption to describe the non-correspondence between 2D and 3D landmarks caused by pose variations and propose a pose adaptive 3DMM fitting algorithm. Secondly, we mesh the whole image into a 3D object and eliminate the pose and expression variations using an identity preserving 3D transformation. Finally, we propose an inpainting method based on Possion Editing to fill the invisible region caused by self occlusion. Extensive experiments on Multi-PIE and LFW demonstrate that the proposed method significantly improves face recognition performance and outperforms state-of-the-art methods in both constrained and unconstrained environments.

@InProceedings{Zhu_2015_CVPR,author = {Zhu, Xiangyu and Lei, Zhen and Yan, Junjie and Yi, Dong and Li, Stan Z.},title = {High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Sparse Convolutional Neural Networks

Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, Marianna Pensky

Abstract: Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel and intra-channel redundancy, with a fine-tuning step that minimize the recognition loss caused by maximizing sparsity. This procedure zeros out more than 90\% of parameters, with a drop of accuracy that is less than 1\% on the ILSVRC2012 dataset.  We also propose an efficient sparse matrix multiplication algorithm on CPU for Sparse Convolutional Neural Networks (SCNN) models. Our CPU implementation demonstrates much higher efficiency than the off-the-shelf sparse matrix libraries, with a significant speedup realized over the original dense network. In addition, we apply the SCNN model to the object detection problem, in conjunction with a cascade model and sparse fully connected layers, to achieve significant speedups.

@InProceedings{Liu_2015_CVPR,author = {Liu, Baoyuan and Wang, Min and Foroosh, Hassan and Tappen, Marshall and Pensky, Marianna},title = {Sparse Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Florian Schroff, Dmitry Kalenichenko, James Philbin

Abstract: Despite significant recent advances in the field of face recognition [DeepFace, DeepId2], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors.  Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. The benefit of our approach is much greater representational efficiency: we achieve state-of-the-art face recognition performance using only 128 bytes per face.  On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves 95.12%. Our system cuts the error rate in comparison to the best published result [DeepId2+] by 30% on both datasets.

@InProceedings{Schroff_2015_CVPR,author = {Schroff, Florian and Kalenichenko, Dmitry and Philbin, James},title = {FaceNet: A Unified Embedding for Face Recognition and Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Cascaded Hand Pose Regression

Xiao Sun, Yichen Wei, Shuang Liang, Xiaoou Tang, Jian Sun

Abstract: We extends the previous 2D cascaded object pose regression work [9] in two aspects so that it works better for 3D articulated objects. Our first contribution is 3D pose-indexed features that generalize the previous 2D parameterized features and achieve better invariance to 3D transformations. Our second contribution is a principled hierarchical regression that is adapted to the articulated object structure. It is therefore more accurate and faster. Comprehensive experiments verify the state-of-the-art accuracy and efficiency of the proposed approach on the challenging 3D hand pose estimation problem, on a public dataset and our new dataset.

@InProceedings{Sun_2015_CVPR,author = {Sun, Xiao and Wei, Yichen and Liang, Shuang and Tang, Xiaoou and Sun, Jian},title = {Cascaded Hand Pose Regression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang

Abstract: Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to fine-tune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Cong and Li, Hongsheng and Wang, Xiaogang and Yang, Xiaokang},title = {Cross-Scene Crowd Counting via Deep Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, Zheng Zhang

Abstract: Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what).  In this paper, we propose to apply visual attention to fine-grained classification task using deep neural network. Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts. We combine these attentions to train domain-specific deep nets, then use it to improve both the what and where aspects. Importantly, we avoid using expensive annotations like bounding box or part information from end-to-end. The weak supervision constraint makes our work easier to generalize.  We have verified the effectiveness of the method on the subsets of ILSVRC2012 dataset and CUB200_2011 dataset. Our pipeline delivered significant improvements and achieved the best accuracy under the weakest supervision condition. The performance is competitive against other methods that rely on additional annotations.

@InProceedings{Xiao_2015_CVPR,author = {Xiao, Tianjun and Xu, Yichong and Yang, Kuiyuan and Zhang, Jiaxing and Peng, Yuxin and Zhang, Zheng},title = {The Application of Two-Level Attention Models in Deep Convolutional Neural Network for Fine-Grained Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Deformable Parts Models and Convolutional Networks each have achieved notable performance in object detection.  Yet these two approaches find their strengths in  complementary areas: DPMs are well-versed in object composition, modeling fine-grained spatial relationships between parts; likewise, ConvNets are adept at producing powerful image features, having been discriminatively trained directly on the pixels. In this paper, we propose a new model that combines these two approaches, obtaining the advantages of each. We train this model using a new structured loss function that considers all bounding boxes within an image, rather than isolated object instances. This enables the non-maximal suppression (NMS) operation, previously treated as a separate post-processing stage, to be integrated into the model. This allows for discriminative training of our combined Convnet + DPM + NMS model in end-to-end fashion. We evaluate our system on PASCAL VOC 2007 and 2011 datasets, achieving competitive results on both bench- marks.

@InProceedings{Wan_2015_CVPR,author = {Wan, Li and Eigen, David and Fergus, Rob},title = {End-to-End Integration of a Convolution Network, Deformable Parts Model and Non-Maximum Suppression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Kuan-Chuan Peng, Tsuhan Chen, Amir Sadovnik, Andrew C. Gallagher

Abstract: This paper explores two new aspects of photos and human emotions. First, we show through psychovisual studies that different people have different emotional reactions to the same image, which is a strong and novel departure from previous work that only records and predicts a single dominant emotion for each image. Our studies also show that the same person may have multiple emotional reactions to one image. Predicting emotions in ``distributions'' instead of a single dominant emotion is important for many applications. Second, we show not only that we can often change the evoked emotion of an image by adjusting color tone and texture related features but also that we can choose in which ``emotional direction" this change occurs by selecting a target image. In addition, we present a new database, Emotion6, containing distributions of emotions.

@InProceedings{Peng_2015_CVPR,author = {Peng, Kuan-Chuan and Chen, Tsuhan and Sadovnik, Amir and Gallagher, Andrew C.},title = {A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Edgar Simo-Serra, Sanja Fidler, Francesc Moreno-Noguer, Raquel Urtasun

Abstract: In this paper, we analyze the fashion of clothing of a large social website. Our goal is to learn and predict how fashionable a person looks on a photograph and suggest subtle improvements the user could make to improve her/his appeal. We propose a Conditional Random Field model that jointly reasons about several fashionability factors such as the type of outfit and garments the user is wearing, the type of the user, the photograph's setting (e.g., the scenery behind the user), and the fashionability score. Importantly, our model is able to give rich feedback back to the user, conveying which garments or even scenery she/he should change in order to improve fashionability. We demonstrate that our joint approach significantly outperforms a variety of intelligent baselines. We additionally collected a novel heterogeneous dataset with 144,169 user posts containing diverse image, textual and meta information which can be exploited for our task. We also provide a detailed analysis of the data, showing different outfit trends and fashionability scores across the globe and across a span of 6 years.

@InProceedings{Simo-Serra_2015_CVPR,author = {Simo-Serra, Edgar and Fidler, Sanja and Moreno-Noguer, Francesc and Urtasun, Raquel},title = {Neuroaesthetics in Fashion: Modeling the Perception of Fashionability},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Anton van den Hengel, Chris Russell, Anthony Dick, John Bastian, Daniel Pooley, Lachlan Fleming, Lourdes Agapito

Abstract: We propose a method to recover the structure of a compound scene from multiple silhouettes. Structure is expressed as a collection of 3D primitives chosen from a pre-defined library, each with an associated pose. This has several advantages over a volume or mesh representation both for estimation and the utility of the recovered model. The main challenge in recovering such a model is the combinatorial number of possible arrangements of parts. We address this issue by exploiting the intrinsic structure and sparsity of the problem, and show that our method scales to scenes constructed from large libraries of parts.

@InProceedings{Hengel_2015_CVPR,author = {van den Hengel, Anton and Russell, Chris and Dick, Anthony and Bastian, John and Pooley, Daniel and Fleming, Lachlan and Agapito, Lourdes},title = {Part-Based Modelling of Compound Scenes From Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Potts energy  frequently occurs in  computer vision applications.  We present an efficient parallel method for optimizing Potts energy based on the extension of hierarchical fusion algorithm.  Unlike previous parallel  graph-cut based optimization algorithms, our approach has optimality bounds even after a single iteration over all labels, i.e. after solving only k-1 max-flow problems, where k is the number of labels. This is perhaps the minimum number of max-flow problems one has to solve to obtain a solution with optimality guarantees.  Our approximation factor is O(log k). Although this is not as good as the factor of 2 approximation of the well known expansion algorithm,  we achieve very good results in practice. In particular, we found that the results of our algorithm after one iteration are always better than the results after one iteration of the expansion algorithm. We demonstrate  experimentally the computational advantages of our parallel implementation on the problem of stereo correspondence, achieving a factor of 1.5 to 2.6 speedup compared to the serial implementation.  These results were obtained with a small number of processors.  The expected speedups with a larger number of processors are greater.

@InProceedings{Veksler_2015_CVPR,author = {Veksler, Olga},title = {Efficient Parallel Optimization for Potts Energy With Hierarchical Fusion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Michael S. Ryoo, Brandon Rothrock, Larry Matthies

Abstract: In this paper, we present a new feature representation for first-person videos. In first-person video understanding (e.g., activity recognition), it is very important to capture both entire scene dynamics (i.e., egomotion) and salient local motion observed in videos. We describe a representation framework based on time series pooling, which is designed to abstract short-term/long-term changes in feature descriptor elements. The idea is to keep track of how descriptor values are changing over time and summarize them to represent motion in the activity video. The framework is general, handling any types of per-frame feature descriptors including conventional motion descriptors like histogram of optical flows (HOF) as well as appearance descriptors from more recent convolutional neural networks (CNN).  We experimentally confirm that our approach clearly outperforms previous feature representations including bag-of-visual-words and improved Fisher vector (IFV) when using identical underlying feature descriptors. We also confirm that our feature representation has superior performance to existing state-of-the-art features like local spatio-temporal features and Improved Trajectory Features (originally developed for 3rd-person videos) when handling first-person videos. Multiple first-person activity datasets were tested under various settings to confirm these findings.

@InProceedings{Ryoo_2015_CVPR,author = {Ryoo, Michael S. and Rothrock, Brandon and Matthies, Larry},title = {Pooled Motion Features for First-Person Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Artiom Kovnatsky, Michael M. Bronstein, Xavier Bresson, Pierre Vandergheynst

Abstract: In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework. We pose the functional correspondence problem as matrix completion with manifold geometric structure and inducing functional localization with the L1 norm. We discuss efficient numerical procedures for the solution of our problem. Our method compares favorably to the accuracy of state-of-the-art correspondence algorithms on non-rigid shape matching benchmarks, and is especially advantageous in settings when only scarce data is available.

@InProceedings{Kovnatsky_2015_CVPR,author = {Kovnatsky, Artiom and Bronstein, Michael M. and Bresson, Xavier and Vandergheynst, Pierre},title = {Functional Correspondence by Matrix Completion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Learning a low-dimensional structure plays an important role in computer vision. Recently, a new family of methods, such as l1 minimization and robust principal component analysis, has been proposed for low-rank matrix approximation problems and shown to be robust against outliers and missing data. But these methods often require heavy computational load and can fail to find a solution when highly corrupted data are presented. In this paper, an elastic-net regularization based low-rank matrix factorization method for subspace learning is proposed. The proposed method finds a robust solution efficiently by enforcing a strong convex constraint to improve the algorithm's stability while maintaining the low-rank property of the solution. It is shown that any stationary point of the proposed algorithm satisfies the Karush-Kuhn-Tucker optimality conditions. The proposed method is applied to a number of low-rank matrix approximation problems to demonstrate its efficiency in the presence of heavy corruptions and to show its effectiveness and robustness compared to the existing methods.

@InProceedings{Kim_2015_CVPR,author = {Kim, Eunwoo and Lee, Minsik and Oh, Songhwai},title = {Elastic-Net Regularization of Singular Values for Robust Subspace Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In recent years, several feature encoding schemes for the bags-of-visual-words model have been proposed. While most of these schemes produce impressive results, they all share an important limitation: their high computational complexity makes it challenging to use them for large-scale problems. In this work, we propose an approximate locality-constrained encoding scheme that offers significantly better computational efficiency (~40x) than its exact counterpart, with comparable classification accuracy. Using the perturbation analysis of least-squares problems, we present a formal approximation error analysis of our approach, which helps distill the intuition behind the robustness of our method. We present a thorough set of empirical analyses on multiple standard data-sets, to assess the capability of our encoding scheme for its representational as well as discriminative accuracy.

@InProceedings{Kuang_2015_CVPR,author = {Kuang, Da and Gittens, Alex and Hamid, Raffay},title = {Hardware Compliant Approximate Image Codes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this paper, we propose a novel uncalibrated photometric method for refining depth maps of multi-albedo objects obtained from consumer depth cameras like Kinect. Existing uncalibrated photometric methods either assume that the object has constant albedo or rely on segmenting images into constant albedo regions. The method of this paper does not require the constant albedo assumption and we believe it is the first work of its kind to handle objects with arbitrary albedo under uncalibrated illumination. We first robustly estimate a rank 3 approximation of the observed brightness matrix using an iterative reweighting method. Subsequently, we factorize this rank reduced brightness matrix into the corresponding lighting, albedo and surface normal components. The proposed factorization is shown to be convergent. We experimentally demonstrate the value of our approach by presenting highly accurate three-dimensional reconstructions of a wide variety of objects. Additionally, since any photometric method requires a radiometric calibration of the camera used, we also present a direct radiometric calibration technique for the infra-red camera of the structured-light stereo depth scanner. Unlike existing methods, this calibration technique does not depend on a known calibration object or on the properties of the scene illumination used.

@InProceedings{Chatterjee_2015_CVPR,author = {Chatterjee, Avishek and Madhav Govindu, Venu},title = {Photometric Refinement of Depth Maps for Multi-Albedo Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We study the problem of predicting the future, though only in the probabilistic sense of estimating a future state of a time-varying probability distribution. This is not only an interesting academic problem, but solving this extrapolation problem also has many practical application, e.g. for training classifiers that have to operate under time-varying conditions. Our main contribution is a method for predicting the next step of the time-varying distribution from a given sequence of sample sets from earlier time steps. For this we rely on two recent machine learning techniques: embedding probability distributions into a reproducing kernel Hilbert space, and learning operators by vector-valued regression. We illustrate the working principles and the practical usefulness of our method by experiments on synthetic and real data. We also highlight an exemplary application: training a classifier in a domain adaptation setting without having access to examples from the test time distribution at training time.

@InProceedings{Lampert_2015_CVPR,author = {Lampert, Christoph H.},title = {Predicting the Future Behavior of a Time-Varying Probability Distribution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Anna Khoreva, Fabio Galasso, Matthias Hein, Bernt Schiele

Abstract: Video segmentation has become an important and active research area with a large diversity of proposed approaches. Graph-based methods, enabling topperformance on recent benchmarks, consist of three essential components: 1. powerful features account for object appearance and motion similarities; 2. spatio-temporal neighborhoods of pixels or superpixels (the graph edges) are modeled using a combination of those features; 3. video segmentation is formulated as a graph partitioning problem. While a wide variety of features have been explored and various graph partition algorithms have been proposed, there is surprisingly little research on how to construct a graph to obtain the best video segmentation performance. This is the focus of our paper. We propose to combine features by means of a classifier, use calibrated classifier outputs as edge weights and define the graph topology by edge selection. By learning the graph (without changes to the graph partitioning method), we improve the results of the best performing video segmentation algorithm by 6% on the challenging VSB100 benchmark, while reducing its runtime by 55%, as the learnt graph is much sparser.

@InProceedings{Khoreva_2015_CVPR,author = {Khoreva, Anna and Galasso, Fabio and Hein, Matthias and Schiele, Bernt},title = {Classifier Based Graph Construction for Video Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, Juan Carlos Niebles

Abstract: In spite of many dataset efforts for human action recognition, current computer vision algorithms are still severely limited in terms of the variability and complexity of the actions that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on simple actions and movements occurring on manually trimmed videos. In this paper, we introduce ActivityNet: a new large-scale video benchmark for human activity understanding. Our new benchmark aims at covering a wide range of complex human activities that are of interest to people in their daily living. In its current version, ActivityNet provides samples from 203 activity categories with an average of 137 untrimmed videos per class and 1.41 activity instances per video, for a total of 849 hours of video. We illustrate three scenarios in which ActivityNet can be used to benchmark and compare algorithms for human activity understanding: untrimmed video classification, trimmed activity classification and activity detection.

@InProceedings{Heilbron_2015_CVPR,author = {Caba Heilbron, Fabian and Escorcia, Victor and Ghanem, Bernard and Carlos Niebles, Juan},title = {ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mid-Level Deep Pattern Mining

Yao Li, Lingqiao Liu, Chunhua Shen, Anton van den Hengel

Abstract: Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activation extracted from the first fully-connected layer of a CNN have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern (See Fig. 1), surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.

@InProceedings{Li_2015_CVPR,author = {Li, Yao and Liu, Lingqiao and Shen, Chunhua and van den Hengel, Anton},title = {Mid-Level Deep Pattern Mining},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hosnieh Sattar, Sabine Muller, Mario Fritz, Andreas Bulling

Abstract: Previous work on predicting the target of visual search from human fixations only considered closed-world settings in which training labels are available and predictions are performed for a known set of potential targets. In this work we go beyond the state of the art by studying search target prediction in an open-world setting in which we no longer assume that we have fixation data to train for the search targets. We present a dataset containing fixation data of 18 users searching for natural images from three image categories within synthesised image collages of about 80 images. In a closed-world baseline experiment we show that we can predict the correct target image out of a candidate set of five images. We then present a new problem formulation for search target prediction in the open-world setting that is based on learning compatibilities between fixations and potential targets.

@InProceedings{Sattar_2015_CVPR,author = {Sattar, Hosnieh and Muller, Sabine and Fritz, Mario and Bulling, Andreas},title = {Prediction of Search Targets From Fixations in Open-World Settings},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited. Aiming at filling this gap, we investigate three key mathematical properties of representations: equivariance, invariance, and equivalence. Equivariance studies how transformations of the input image are encoded by the representation, invariance being a special case where a transformation has no effect. Equivalence studies whether two representations, for example two different parametrisations of a CNN, capture the same visual information or not. A number of methods to establish these properties empirically are proposed, including introducing transformation and stitching layers in CNNs. These methods are then applied to popular representations to reveal insightful aspects of their structure, including clarifying at which layers in a CNN certain geometric invariances are achieved. While the focus of the paper is theoretical, direct applications to structured-output regression are demonstrated too.

@InProceedings{Lenc_2015_CVPR,author = {Lenc, Karel and Vedaldi, Andrea},title = {Understanding Image Representations by Measuring Their Equivariance and Equivalence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Dongliang Cheng, Brian Price, Scott Cohen, Michael S. Brown

Abstract: Illumination estimation is the process of determining the chromaticity of the illumination in an imaged scene in order to remove undesirable color casts through white-balancing.   While computational color constancy is a well-studied topic in computer vision, it remains challenging due to the ill-posed nature of the problem.   One class of techniques relies on low-level statistical information in the image color distribution and works under various assumptions (e.g. Grey-World, White-Patch, etc). These methods have an advantage that they are simple and fast, but often do not perform well.  More recent state-of-the-art methods employ learning-based techniques that produce better results, but often rely on complex features and have long evaluation and training times.  In this paper, we present a learning-based method based on four simple color features and show how to use this with an ensemble of regression trees to estimate the illumination.  We demonstrate that our approach is not only faster than existing learning-based methods in terms of both evaluation and training time, but also gives the best results reported to date on modern color constancy data sets.

@InProceedings{Cheng_2015_CVPR,author = {Cheng, Dongliang and Price, Brian and Cohen, Scott and Brown, Michael S.},title = {Effective Learning-Based Illuminant Estimation Using Simple Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Johannes L. Schonberger, Alexander C. Berg, Jan-Michael Frahm

Abstract: Large-scale Structure-from-Motion systems typically spend major computational effort on pairwise image matching and geometric verification in order to discover connected components in large-scale, unordered image collections. In recent years, the research community has spent significant effort on improving the efficiency of this stage. In this paper, we present a comprehensive overview of various state-of-the-art methods, evaluating and analyzing their performance. Based on the insights of this evaluation, we propose a learning-based approach, the PAirwise Image Geometry Encoding (PAIGE), to efficiently identify image pairs with scene overlap without the need to perform exhaustive putative matching and geometric verification. PAIGE achieves state-of-the-art performance and integrates well into existing Structure-from-Motion pipelines.

@InProceedings{Schonberger_2015_CVPR,author = {Schonberger, Johannes L. and Berg, Alexander C. and Frahm, Jan-Michael},title = {PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-From-Motion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper proposes a simple method for estimating dense and accurate optical flow field. It revitalizes an early idea of piecewise parametric flow model. A key innovation is that, we fit a flow field piecewise to a variety of parametric models, where the domain of each piece (i.e., each piece's shape, position and size) is determined adaptively, while at the same time maintaining a global inter-piece flow continuity constraint. We achieve this by a multi-model fitting scheme via energy minimization. Our energy takes into account both the piecewise constant model assumption and the flow field continuity constraint, enabling the proposed method to effectively handle both homogeneous motions and complex motions. The experiments on three public optical flow benchmarks (KITTI, MPI Sintel, and Middlebury) show the superiority of our method compared with the state of the art: it achieves top-tier performances on all the three benchmarks.

@InProceedings{Yang_2015_CVPR,author = {Yang, Jiaolong and Li, Hongdong},title = {Dense, Accurate Optical Flow Estimation With Piecewise Parametric Model},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Soonmin Hwang, Jaesik Park, Namil Kim, Yukyung Choi, In So Kweon

Abstract: With the increasing interest in pedestrian detection, pedestrian datasets have also been the subject of research in the past decades. However, most existing datasets focus on a color channel, while a thermal channel is helpful for detection even in a dark environment. With this in mind, we propose a multispectral pedestrian dataset which provides well aligned color-thermal image pairs, captured by beam splitter-based special hardware. The color-thermal dataset is as large as previous color-based datasets and provides dense annotations including temporal correspondences. With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs. Multispectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.

@InProceedings{Hwang_2015_CVPR,author = {Hwang, Soonmin and Park, Jaesik and Kim, Namil and Choi, Yukyung and So Kweon, In},title = {Multispectral Pedestrian Detection: Benchmark Dataset and Baseline},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: The camera response function (CRF) relates quantised image pixel values with physical incoming light. This paper describes a method to estimate the CRF from a single image of a general two-coloured surface for which the albedo ratio between the coloured regions is known a priori. While other radiometric calibration methods either use multiple frames or require the light to be infinitely distant, the algorithm herein proposed makes no assumptions about lighting conditions and can handle cameras with strong vignetting. Although the approach is generic, in the sense that can be applied to any camera system, the method is particularly well suited for determining the CRF of near-lighting endoscopes in the operating room. This is a very pertinent problem for which no practical, effective solutions have been proposed. The robustness, repeatability, and accuracy of the algorithm is experimentally validated in real images acquired with different endoscopic set-ups.

@InProceedings{Rodrigues_2015_CVPR,author = {Rodrigues, Pedro and Barreto, Joao P.},title = {Single-Image Estimation of the Camera Response Function in Near-Lighting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jimmy Addison Lee, Jun Cheng, Beng Hai Lee, Ee Ping Ong, Guozhen Xu, Damon Wing Kee Wong, Jiang Liu, Augustinus Laude, Tock Han Lim

Abstract: Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are often being exploited, they do not work very well upon unhealthy multimodal images with severe diseases. Additionally, the descriptors demand high dimensionality to adequately represent the features of interest. The higher the dimensionality, the greater the consumption of resources (e.g. memory space). To this end, this paper introduces a novel registration algorithm coined low-dimensional step pattern analysis (LoSPA), tailored to achieve low dimensionality while providing sufficient distinctiveness to effectively align unhealthy multimodal image pairs. The algorithm locates hypotheses of robust corner features based on connecting edges from the edge maps, mainly formed by vascular junctions. This method is insensitive to intensity changes, and produces uniformly distributed features and high repeatability across the image domain. The algorithm continues with describing the corner features in a rotation invariant manner using step patterns. These customized step patterns are robust to non-linear intensity changes, which are well-suited for multimodal retinal image registration. Apart from its low dimensionality, the LoSPA algorithm achieves about two-fold higher success rate in multimodal registration on the dataset of severe retinal diseases when compared to the top score among state-of-the-art algorithms.

@InProceedings{Lee_2015_CVPR,author = {Addison Lee, Jimmy and Cheng, Jun and Hai Lee, Beng and Ping Ong, Ee and Xu, Guozhen and Wing Kee Wong, Damon and Liu, Jiang and Laude, Augustinus and Han Lim, Tock},title = {A Low-Dimensional Step Pattern Analysis Algorithm With Application to Multimodal Retinal Image Registration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper proposes a novel approach to action recognition from RGB-D cameras, in which depth features and RGB visual features are jointly used. Rich heterogeneous RGB and depth data are effectively compressed and projected to a learned shared space, in order to reduce noise and capture useful information for recognition. Knowledge from various sources can then be shared with others in the learned space to learn cross-modal features. This guides the discovery of valuable information for recognition. To capture complex spatiotemporal structural relationships in visual and depth features, we represent both RGB and depth data in a matrix form. We formulate the recognition task as a low-rank bilinear model composed of row and column parameter matrices. The rank of the model parameter is minimized to build a low-rank classifier, which is beneficial for improving the generalization power. The proposed method is extensively evaluated on two public RGB-D action datasets, and achieves state-of-the-art results. It also shows promising results if RGB or depth data are missing in training or testing procedure.

@InProceedings{Kong_2015_CVPR,author = {Kong, Yu and Fu, Yun},title = {Bilinear Heterogeneous Information Machine for RGB-D Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


MRF Optimization by Graph Approximation

Abstract: Graph cuts-based algorithms have achieved great success in energy minimization for many computer vision applications. These algorithms provide approximated solutions for multi-label energy functions via move-making approach. This approach fuses the current solution with a proposal to generate a lower-energy solution. Thus, generating the appropriate proposals is necessary for the success of the move-making approach. However, not much research efforts has been done on the generation of ``good'' proposals, especially for non-metric energy functions. In this paper, we propose an application-independent and energy-based approach to generate ``good'' proposals. With these proposals, we present a graph cuts-based move-making algorithm called GA-fusion (fusion with graph approximation-based proposals). Extensive experiments support that our proposal generation is effective across different classes of energy functions. The proposed algorithm outperforms others both on real and synthetic problems.

@InProceedings{Kim_2015_CVPR,author = {Kim, Wonsik and Mu Lee, Kyoung},title = {MRF Optimization by Graph Approximation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hakan Bilen, Marco Pedersoli, Tinne Tuytelaars

Abstract: Weakly supervised object detection, is a challenging task, where the training procedure involves learning at the same time both, the model appearance and the object location in each image. The classical approach to solve this problem is to consider the location of the object of interest in each image as a latent variable and minimize the loss generated by such latent variable during learning.  However, as learning appearance and localization are two interconnected tasks, the optimization is not convex and the procedure can easily get stuck in a poor local minimum, the algorithm "misses" the object in some images. In this paper, we help the optimization to get close to the global minimum by enforcing a "soft" similarity between each possible location in the image and a reduced set of "exemplars", or clusters, learned with a convex formulation in the training images.  The help is effective because it comes from a different and smooth source of information that is not directly connected with the main task.  Results show that our method improves a strong baseline based on convolutional neural network features by more than 4 points without any additional features or extra computation at testing time but only adding a small increment of the training time due to the convex clustering.

@InProceedings{Bilen_2015_CVPR,author = {Bilen, Hakan and Pedersoli, Marco and Tuytelaars, Tinne},title = {Weakly Supervised Object Detection With Convex Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


SALICON: Saliency in Context

Ming Jiang, Shengsheng Huang, Juanyong Duan, Qi Zhao

Abstract: Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention. This paper presents a new method to collect large-scale human data during natural explorations on images. While current datasets present a rich set of images and task-specific annotations such as category labels and object segments, this work focuses on recording and logging how humans shift their attention during visual exploration. The goal is to offer new possibilities to (1) complement task-specific annotations to advance the ultimate goal in visual understanding, and (2) understand visual attention and learn saliency models, all with human attentional data at a much larger scale.  We designed a mouse-contingent multi-resolutional paradigm based on neurophysiological and psychophysical studies of peripheral vision, to simulate the natural viewing behavior of humans. The new paradigm allowed using a general-purpose mouse instead of an eye tracker to record viewing behaviors, thus enabling large-scale data collection. The paradigm was validated with controlled laboratory as well as large-scale online data. We report in this paper a proof-of-concept SALICON dataset of human "free-viewing" data on 10,000 images from the Microsoft COCO (MS COCO) dataset with rich contextual information. We evaluated the use of the collected data in the context of saliency prediction, and demonstrated them a good source as ground truth for the evaluation of saliency algorithms.

@InProceedings{Jiang_2015_CVPR,author = {Jiang, Ming and Huang, Shengsheng and Duan, Juanyong and Zhao, Qi},title = {SALICON: Saliency in Context},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers

Abstract: Despite tremendous progress in computer vision, effective learning on very large-scale (>100K patients) medical image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system. Instead of using full 3D medical volumes, we focus on a collection of representative ~216K 2D key images/slices (selected by clinicians for diagnostic reference) with text-driven scalar and vector labels. Our system interleaves between unsupervised learning (e.g., latent Dirichlet allocation, recurrent neural net language models) on document- and sentence-level texts to generate semantic labels and supervised learning via deep convolutional neural networks (CNNs) to map from images to label spaces. Disease-related key words can be predicted for radiology images in a retrieval manner. We have demonstrated promising quantitative and qualitative results. The large-scale datasets of extracted key images and their categorization, embedded vector labels and sentence descriptions can be harnessed to alleviate the deep learning "data-hungry" obstacle in the medical domain.

@InProceedings{Shin_2015_CVPR,author = {Shin, Hoo-Chang and Lu, Le and Kim, Lauren and Seff, Ari and Yao, Jianhua and Summers, Ronald M.},title = {Interleaved Text/Image Deep Mining on a Very Large-Scale Radiology Database},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Vignesh Ramanathan, Congcong Li, Jia Deng, Wei Han, Zhen Li, Kunlong Gu, Yang Song, Samy Bengio, Charles Rosenberg, Li Fei-Fei

Abstract: Human actions capture a wide variety of interactions between people and objects. As a result, the set of possible actions is extremely large and it is difficult to obtain sufficient training examples for all actions. However, we could compensate for this sparsity in supervision by leveraging the rich semantic relationship between different actions. A single action is often composed of other smaller actions and is exclusive of certain others. We need a method which can reason about such relationships and extrapolate unobserved actions from known actions. Hence, we propose a novel neural network framework which jointly extracts the relationship between actions and uses them for training better action retrieval models. Our model incorporates linguistic, visual and logical consistency based cues to effectively identify these relationships. We train and test our model on a largescale image dataset of human actions. We show a significant improvement in mean AP compared to different baseline methods including the HEX-graph approach from Deng et al.

@InProceedings{Ramanathan_2015_CVPR,author = {Ramanathan, Vignesh and Li, Congcong and Deng, Jia and Han, Wei and Li, Zhen and Gu, Kunlong and Song, Yang and Bengio, Samy and Rosenberg, Charles and Fei-Fei, Li},title = {Learning Semantic Relationships for Better Action Retrieval in Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Bo Li, Chunhua Shen, Yuchao Dai, Anton van den Hengel, Mingyi He

Abstract: Predicting the depth (or surface normal) of a scene from single monocular color images is a challenging task. This paper tackles this challenging and essentially under-determined problem by regression on deep convolutional neural network (DCNN) features, combined with a post-processing refining step using conditional random fields(CRF). Our framework works at two levels, super-pixel level and pixel level. First, we design a DCNN model to learn the mapping from multi-scale image patches to depth or surface normal values at the super-pixel level. Second, the estimated super-pixel depth or surface normal is refined to the pixel level by exploiting various potentials on the depth or surface normal map, which includes a data term, a smoothness term among super-pixels and an auto-regression term characterizing the local structure of the estimation map. The inference problem can be efficiently solved because it admits a closed-form solution. Experiments on the Make3D and NYU Depth V2 datasets show competitive results compared with recent state-of-the-art methods.

@InProceedings{Li_2015_CVPR,author = {Li, Bo and Shen, Chunhua and Dai, Yuchao and van den Hengel, Anton and He, Mingyi},title = {Depth and Surface Normal Estimation From Monocular Images Using Regression on Deep Features and Hierarchical CRFs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Human actions can be represented by the trajectories of skeleton joints. Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers. In this paper, considering that recurrent neural network (RNN) can model the long-term contextual information of temporal sequences well, we propose an end-to-end hierarchical RNN for skeleton based action recognition. Instead of taking the whole skeleton as the input, we divide the human skeleton into five parts according to human physical structure, and then separately feed them to five subnets. As the number of layers increases, the representations extracted by the subnets are hierarchically fused to be the inputs of higher layers. The final representations of the skeleton sequences are fed into a single-layer perceptron, and the temporally accumulated output of the perceptron is the final decision. We compare with five other deep RNN architectures derived from our model to verify the effectiveness of the proposed network, and also compare with several other methods on three publicly available datasets. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.

@InProceedings{Du_2015_CVPR,author = {Du, Yong and Wang, Wei and Wang, Liang},title = {Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Estimating surface normals from just a single image is challenging. To simplify the problem, previous work focused on special cases, including directional lighting, known reflectance maps, etc., making shape from shading impractical outside the lab. To cope with more realistic settings, shading cues need to be combined and generalized to natural illumination. This significantly increases the complexity of the approach, as well as the number of parameters that require tuning. Enabled by a new large-scale dataset for training and analysis, we address this with a discriminative learning approach to shape from shading, which uses regression forests for efficient pixel-independent prediction and fast learning. Von Mises-Fisher distributions in the leaves of each tree enable the estimation of surface normals. To account for their expected spatial regularity, we introduce spatial features, including texton and silhouette features. The proposed silhouette features are computed from the occluding contours of the surface and provide scale-invariant context. Aside from computational efficiency, they enable good generalization to unseen data and importantly allow for a robust estimation of the reflectance map, extending our approach to the uncalibrated setting. Experiments show that our discriminative approach outperforms state-of-the-art methods on synthetic and real-world datasets.

@InProceedings{Richter_2015_CVPR,author = {Richter, Stephan R. and Roth, Stefan},title = {Discriminative Shape From Shading in Uncalibrated Illumination},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jiwen Lu, Gang Wang, Weihong Deng, Pierre Moulin, Jie Zhou

Abstract: In this paper, we propose a multi-manifold deep metric learning (MMDML) method for image set classification, which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations. Motivated by the fact that manifold can be effectively used to model the nonlinearity of samples in each image set and deep learning has demonstrated superb capability to model the nonlinearity of samples, we propose a MMDML method to learn multiple sets of nonlinear transformations, one set for each object class, to nonlinearly map multiple sets of image instances into a shared feature subspace, under which the manifold margin of different class is maximized, so that both discriminative and class-specific information can be exploited, simultaneously. Our method achieves the state-of-the-art performance on five widely used datasets.

@InProceedings{Lu_2015_CVPR,author = {Lu, Jiwen and Wang, Gang and Deng, Weihong and Moulin, Pierre and Zhou, Jie},title = {Multi-Manifold Deep Metric Learning for Image Set Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Afshin Dehghan, Yicong Tian, Philip H. S. Torr, Mubarak Shah

Abstract: In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously. Our method allows us to overcome the confinements of data association based MOT approaches; where the performance is dependent on the object detection results provided at input level. At the core of our method lies structured learning which learns a model for each target and infers the best location of all targets simultaneously in a video clip. The inference of our structured learning is done through a new Target Identity-aware Network Flow (TINF), where each node in the network encodes the probability of each target identity belonging to that node. The proposed Lagrangian relaxation optimization finds the high quality solution to the network. During optimization a soft spatial constraint is enforced between the nodes of the graph which helps reducing the ambiguity caused by nearby targets with similar appearance in crowded scenarios. We show that automatically detecting and tracking targets in a single framework can help resolve the ambiguities due to frequent occlusion and heavy articulation of targets. Our experiments involve challenging yet distinct datasets and show that our method can achieve results better than the state-of-art.

@InProceedings{Dehghan_2015_CVPR,author = {Dehghan, Afshin and Tian, Yicong and Torr, Philip H. S. and Shah, Mubarak},title = {Target Identity-Aware Network Flow for Online Multiple Target Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chung-Ching Lin, Sharathchandra U. Pankanti, Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin

Abstract: The goal of image stitching is to create natural-looking mosaics free of artifacts that may occur due to relative camera motion, illumination changes, and optical aberrations. In this paper, we propose a novel stitching method, that uses a smooth stitching field over the entire target image, while accounting for all the local transformation variations. Computing the warp is fully automated and uses a combination of local homography and global similarity transformations, both of which are estimated with respect to the target. We mitigate the perspective distortion in the non-overlapping regions by linearizing the homography and slowly changing it to the global similarity. The proposed method is easily generalized to multiple images, and allows one to automatically obtain the best perspective in the panorama. It is also more robust to parameter selection, and hence more automated compared with state-of-the-art methods. The benefits of this method are demonstrated using a variety of challenging cases.

@InProceedings{Lin_2015_CVPR,author = {Lin, Chung-Ching and Pankanti, Sharathchandra U. and Natesan Ramamurthy, Karthikeyan and Aravkin, Aleksandr Y.},title = {Adaptive As-Natural-As-Possible Image Stitching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid

Abstract: We propose a novel approach for optical flow estimation, targeted at large displacements with significant occlusions. It consists of two steps: i) dense matching by edge-preserving interpolation from a sparse set of matches; ii) variational energy minimization initialized with the dense matches. The sparse-to-dense interpolation relies on an appropriate choice of the distance, namely an edge-aware geodesic distance. This distance is tailored to handle occlusions and motion boundaries - two common and difficult issues for optical flow computation. We also propose an approximation scheme for the geodesic distance to allow fast computation without loss of performance. Subsequent to the dense interpolation step, standard one-level variational energy minimization is carried out on the dense matches to obtain the final flow estimation. The proposed approach, called Edge-Preserving Interpolation of Correspondences (EpicFlow) is fast and robust to large displacements. It significantly outperforms the state of the art on MPI-Sintel and performs on par on Kitti and Middlebury.

@InProceedings{Revaud_2015_CVPR,author = {Revaud, Jerome and Weinzaepfel, Philippe and Harchaoui, Zaid and Schmid, Cordelia},title = {EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Gong Cheng, Junwei Han, Lei Guo, Tianming Liu

Abstract: Part model-based methods have been successfully applied to object detection and scene classification and have achieved state-of-the-art results. More recently the "sparselets" work [1-3] were introduced to serve as a universal set of shared basis learned from a large number of part detectors, resulting in notable speedup. Inspired by this framework, in this paper, we propose a novel scheme to train more effective sparselets with a coarse-to-fine framework. Specifically, we first train coarse sparselets to exploit the redundancy existing among part detectors by using an unsupervised single-hidden layer auto-encoder. Then, we simultaneously train fine sparselets and activation vectors using a supervised single-hidden-layer neural network, in which sparselets training and discriminative activation vectors learning are jointly embedded into a unified framework. In order to adequately explore the discriminative information hidden in the part detectors and to achieve sparsity, we propose to optimize a new discriminative objective function by imposing L0-norm sparsity constraint on the activation vectors. By using the proposed framework, promising results for multi-class object detection and scene classification are achieved on PASCAL VOC 2007, MIT Scene-67, and UC Merced Land Use datasets, compared with the existing sparselets baseline methods.

@InProceedings{Cheng_2015_CVPR,author = {Cheng, Gong and Han, Junwei and Guo, Lei and Liu, Tianming},title = {Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tinghui Zhou, Yong Jae Lee, Stella X. Yu, Alyosha A. Efros

Abstract: Given a set of poorly aligned images of the same visual concept without any annotations, we propose an algorithm to jointly bring them into pixel-wise correspondence by estimating a FlowWeb representation of the image set. FlowWeb is a fully-connected correspondence flow graph with each node representing an image, and each edge representing the correspondence flow field between a pair of images, i.e. a vector field indicating how each pixel in one image can find a corresponding pixel in the other image. Correspondence flow is related to optical flow but allows for correspondences between visually dissimilar regions if there is evidence they correspond transitively on the graph. Our algorithm starts by initializing all edges of this complete graph with an off-the-shelf, pairwise flow method. We then iteratively update the graph to force it to be more self- consistent. Once the algorithm converges, dense, globally-consistent correspondences can be read off the graph. Our results suggest that FlowWeb improves alignment accuracy over previous pairwise as well as joint alignment methods.

@InProceedings{Zhou_2015_CVPR,author = {Zhou, Tinghui and Jae Lee, Yong and Yu, Stella X. and Efros, Alyosha A.},title = {FlowWeb: Joint Image Set Alignment by Weaving Consistent, Pixel-Wise Correspondences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Continuous Visibility Feature

Guilin Liu, Yotam Gingold, Jyh-Ming Lien

Abstract: In this work, we propose a new type of visibility measurement named Continuous Visibility Feature (CVF). We say that a point q on the mesh is continuously visible from another point p if there exists a geodesic path connecting p and q that is entirely visible by p. In order to efficiently estimate the continuous visibility for all the vertices in a model, we propose two approaches that use specific CVF properties to avoid exhaustive visibility tests. CVF is then measured as the area of the continuously visible region. With this stronger visibility measure, we show that CVF better encodes the surface and part information of mesh than the tradition line-of-sight based visibility. For example, we show that existing segmentation algorithms can generate better segmentation results using CVF and its variants than using other visibility-based shape descriptors, such as shape diameter function. Similar to visibility and other mesh surface features, continuous visibility would have many applications.

@InProceedings{Liu_2015_CVPR,author = {Liu, Guilin and Gingold, Yotam and Lien, Jyh-Ming},title = {Continuous Visibility Feature},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: The analysis of deformable 3D shape is often cast in terms of the shape's intrinsic geometry due to its invariance to a wide range of non-rigid deformations. However,  object's plasticity in non-rigid transformation often results in transformations that are not completely isometric in the surface's geometry and whose mode of deviation from isometry is an identifiable characteristic of the shape and its deformation modes. In this paper, we propose a novel generative model of the variations of the intrinsic metric of deformable shapes, based on the spectral decomposition of the Laplace-Beltrami operator. To this end, we assume two independent models for the eigenvectors and  the eigenvalues of the graph-Laplacian of a 3d mesh which are learned in a supervised way from a set of shapes belonging to the same class. We show how this model can be efficiently learned given a set of 3D meshes, and evaluate the performance of the resulting generative model in shape classification and retrieval tasks. Comparison with state-of-the-art solutions for these problems confirm the validity of the approach.

@InProceedings{Gasparetto_2015_CVPR,author = {Gasparetto, Andrea and Torsello, Andrea},title = {A Statistical Model of Riemannian Metric Variation for Deformable Shape Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Minsu Cho, Suha Kwak, Cordelia Schmid, Jean Ponce

Abstract: This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes. The setting of this problem is fully unsupervised, without even image-level annotations or any assumption of a single dominant class.  This is far more general than typical colocalization, cosegmentation, or weakly-supervised localization tasks.  We tackle the discovery and localization problem using a part-based region matching approach: We use off-the-shelf region proposals to form a set of candidate bounding boxes for objects and object parts. These regions are efficiently matched across images using a probabilistic Hough transform that evaluates the confidence for each candidate correspondence considering both appearance and spatial consistency. Dominant objects are discovered and localized by comparing the scores of candidate regions and selecting those that stand out over other regions containing them. Extensive experimental evaluations on standard benchmarks demonstrate that the proposed approach significantly outperforms the current state of the art in colocalization, and achieves robust object discovery in challenging mixed-class datasets.

@InProceedings{Cho_2015_CVPR,author = {Cho, Minsu and Kwak, Suha and Schmid, Cordelia and Ponce, Jean},title = {Unsupervised Object Discovery and Localization in the Wild: Part-Based Matching With Bottom-Up Region Proposals},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xiantong Zhen, Zhijie Wang, Mengyang Yu, Shuo Li

Abstract: Descriptor learning has recently drawn increasing attention in computer vision, Existing algorithms are mainly developed for classification rather than for regression which however has recently emerged as a powerful tool to solve a broad range of problems, e.g., head pose estimation. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm to establish a discriminative and compact feature representation for multi-output regression. By formulating as generalized low-rank approximations of matrices with a supervised manifold regularization (SMR), the SDL removes irrelevant and redundant information from raw features by transforming into a low-dimensional space under the supervision of multivariate targets. The obtained discriminative while compact descriptor largely reduces the variability and ambiguity in multi-output regression, and therefore enables more accurate and efficient multivariate estimation. We demonstrate the effectiveness of the proposed SDL algorithm on a representative multi-output regression task: head pose estimation using the benchmark Pointing'04 dataset. Experimental results show that the SDL can achieve high pose estimation accuracy and significantly outperforms state-of-the-art algorithms by an error reduction up to 27.5%. The proposed SDL algorithm provides a general descriptor learning framework in a supervised way for multi-output regression which can largely boost the performance of existing multi-output regression tasks.

@InProceedings{Zhen_2015_CVPR,author = {Zhen, Xiantong and Wang, Zhijie and Yu, Mengyang and Li, Shuo},title = {Supervised Descriptor Learning for Multi-Output Regression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Fillipe Souza, Sudeep Sarkar, Anuj Srivastava, Jingyong Su

Abstract: Graph-theoretical methods have successfully provided semantic and structural interpretations of images and videos. A recent paper introduced a pattern-theoretic approach that allows construction of flexible graphs for representing interactions of actors with objects and inference is accomplished by an efficient annealing algorithm. Actions and objects are termed generators and their interactions are termed bonds; together they form high-probability configurations, or interpretations, of observed scenes. This work and other structural methods have generally been limited to analyzing short videos involving isolated actions. Here we provide an extension that uses additional temporal bonds across individual actions to enable semantic interpretations of longer videos. Longer temporal connections improve scene interpretations as they help discard (temporally) local solutions in favor of globally superior ones. Using this extension, we demonstrate improvements in understanding longer videos, compared to individual interpretations of non-overlapping time segments. We verified the success of our approach by generating interpretations for more than 700 video segments from the YouCook data set, with intricate videos that exhibit cluttered background, scenarios of occlusion,  viewpoint variations and changing conditions of illumination. Interpretations for long video segments were able to yield performance increases of about 70 and, in addition, proved to be more robust to different severe scenarios of classification errors.

@InProceedings{Souza_2015_CVPR,author = {Souza, Fillipe and Sarkar, Sudeep and Srivastava, Anuj and Su, Jingyong},title = {Temporally Coherent Interpretations for Long Videos Using Pattern Theory},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Srikumar Ramalingam, Michel Antunes, Dan Snow, Gim Hee Lee, Sudeep Pillai

Abstract: We propose a simple and useful idea based on cross-ratio constraint for wide-baseline matching and 3D reconstruction. Most existing methods exploit feature points and planes from images. Lines have always been considered notorious for both matching and reconstruction due to the lack of good line descriptors. We propose a method to generate and match new points using virtual lines constructed using pairs of keypoints, which are obtained using standard feature point detectors. We use cross-ratio constraints to obtain an initial set of new point matches, which are subsequently used to obtain line correspondences. We develop a method that works for both calibrated and uncalibrated camera configurations. We show compelling line-matching and large-scale 3D reconstruction.

@InProceedings{Ramalingam_2015_CVPR,author = {Ramalingam, Srikumar and Antunes, Michel and Snow, Dan and Hee Lee, Gim and Pillai, Sudeep},title = {Line-Sweep: Cross-Ratio For Wide-Baseline Matching and 3D Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Victor Escorcia, Juan Carlos Niebles, Bernard Ghanem

Abstract: One of the cornerstone  principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from `simpler' ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we  uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.

@InProceedings{Escorcia_2015_CVPR,author = {Escorcia, Victor and Carlos Niebles, Juan and Ghanem, Bernard},title = {On the Relationship Between Visual Attributes and Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Gucan Long, Laurent Kneip, Xin Li, Xiaohu Zhang, Qifeng Yu

Abstract: We propose a novel approach to compute the camera pose with respect to a reference object given only mirrored views. The latter originate from a planar mirror at different unknown poses. This problem is highly relevant in several extrinsic camera calibration scenarios, where the camera cannot see the reference object directly. In contrast to numerous existing methods, our approach does not employ the fixed axis rotation constraint, but represents a more elegant formulation as a rotation averaging problem. Our theoretical contribution extends the applicability of rotation averaging to a more general case, and enables mirror-based pose estimation in closed-form under the chordal L2-metric, or in an outlier-robust way by employing iterative L1-norm averaging. We demonstrate the advantages of our approach on both synthetic and real data, and show how the method can be applied to calibrate the non-overlapping pair of cameras of a common smart phone.

@InProceedings{Long_2015_CVPR,author = {Long, Gucan and Kneip, Laurent and Li, Xin and Zhang, Xiaohu and Yu, Qifeng},title = {Simplified Mirror-Based Camera Pose Computation via Rotation Averaging},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Complex geometric structural variations of 3D models usually pose great challenges in 3D shape matching and retrieval. In this paper, we propose a high-level shape feature learning scheme to extract deformation-insensitive feature via a novel discriminative deep auto-encoder. First, we developed a multiscale shape distribution to concisely describe the entire shape of a 3D object. Then, by imposing the Fisher discrimination criterion on the neurons in the hidden layer, we developed a novel discriminative deep auto-encoder for shape feature learning. Finally, the neurons in hidden layers from multiple discriminative auto-encoders are concatenated to form a shape descriptor for 3D shape matching and retrieval. The proposed method is evaluated on the representative datasets with large geometric variations, i.e., Mcgill, SHREC'10 ShapeGoogle datasets. Experimental results on the benchmark datasets demonstrate the effectiveness of the proposed method on the applications of 3D shape matching and retrieval.

@InProceedings{Xie_2015_CVPR,author = {Xie, Jin and Fang, Yi and Zhu, Fan and Wong, Edward},title = {DeepShape: Deep Learned Shape Descriptor for 3D Shape Matching and Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}