Jing Zhang, Yang Cao, Shuai Fang, Yu Kang, Chang Wen Chen

Abstract: In this paper, we address a haze removal problem from a single nighttime image, even in the presence of varicolored and non-uniform illumination. The core idea lies in a novel maximum reflectance prior. We first introduce the nighttime hazy imaging model, which includes a local ambient illumination item in both direct attenuation term and scattering term. Then, we propose a simple but effective image prior, maximum reflectance prior, to estimate the varying ambient illumination. The maximum reflectance prior is based on a key observation: for most daytime haze-free image patches, each color channel has very high intensity at some pixels. For the nighttime haze image, the local maximum intensities at each color channel are mainly contributed by the ambient illumination. Therefore, we can directly estimate the ambient illumination and transmission map, and consequently restore a high quality haze-free image. Experimental results on various nighttime hazy images demonstrate the effectiveness of the proposed approach. In particular, our approach has the advantage of computational efficiency, which is 10-100 times faster than state-of-the-art methods.

@InProceedings{Zhang_2017_CVPR,author = {Zhang, Jing and Cao, Yang and Fang, Shuai and Kang, Yu and Wen Chen, Chang},title = {Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}}


Abstract: Many man-made objects have intrinsic symmetries and Manhattan structure. By assuming an orthographic projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, which occur when the input is single- or multiple-image from the same category, e.g., multiple different cars. Specifically, analysis on the single image case implies that Manhattan alone is sufficient to recover the camera projection, and then the 3D structure can be reconstructed uniquely exploiting symmetry. However, Manhattan structure can be difficult to observe from a single image due to occlusion. To this end, we extend to the multiple-image case which can also exploit symmetry but does not require Manhattan axes. We propose a novel rigid structure from motion method, exploiting symmetry and using multiple images from the same category as input. Experimental results on the Pascal3D+ dataset show that our method significantly outperforms baseline methods.

@InProceedings{Gao_2017_CVPR,author = {Gao, Yuan and Yuille, Alan L.},title = {Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation From Single and Multiple Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}}


Abstract: In linear representation-based image classification, an unlabeled sample is represented by the entire training set. To obtain a stable and discriminative solution, regularization on the vector of representation coefficients is necessary. For example, the representation in sparse representation-based classification (SRC) uses L1 norm penalty as regularization, which is equal to lasso. However, lasso overemphasizes the role of sparseness while ignoring the inherent structure among samples belonging to a same class. Many recent developed representation classifications have adopted lasso-type regressions to improve the performance. In this paper, we propose the adaptive class preserving representation for classification (ACPRC). Our method is related to group lasso based classification but different in two key points: When training samples in a class are uncorrelated, ACPRC turns into SRC; when samples in a class are highly correlated, it obtains similar result as group lasso. The superiority of ACPRC over other state-of-the-art regularization techniques including lasso, group lasso, sparse group lasso, etc. are evaluated by extensive experiments.

@InProceedings{Mi_2017_CVPR,author = {Mi, Jian-Xun and Fu, Qiankun and Li, Weisheng},title = {Adaptive Class Preserving Representation for Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}}


Xun Sun, Yuanfan Xie, Pei Luo, Liang Wang

Abstract: A novel dataset for benchmarking image-based localization is presented. With increasing research interests in visual place recognition and localization, several datasets have been published in the past few years. One of the evident limitations of existing datasets is that precise ground truth camera poses of query images are not available in a meaningful 3D metric system. This is in part due to the underlying 3D models of these datasets are reconstructed from Structure from Motion methods. So far little attention has been paid to metric evaluations of localization accuracy. In this paper we address the problem of whether state-of-the-art visual localization techniques can be applied to tasks with demanding accuracy requirements. We acquired training data for a large indoor environment with cameras and a LiDAR scanner. In addition, we collected over 2000 query images with cell phone cameras. Using LiDAR point clouds as a reference, we employed a semi-automatic approach to estimate the 6 degrees of freedom camera poses precisely in the world coordinate system. The proposed dataset enables us to quantitatively assess the performance of various algorithms using a fair and intuitive metric.

@InProceedings{Sun_2017_CVPR,author = {Sun, Xun and Xie, Yuanfan and Luo, Pei and Wang, Liang},title = {A Dataset for Benchmarking Image-Based Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}}


Yongqiang Zhang, Daming Shi, Junbin Gao, Dansong Cheng

Abstract: Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications. The state-of-the-art methods have studied low-rank regression models that are robust against typical noises (like Gaussian noise and out-sample sparse noise) or outliers, such that a regression model can be learned from clean data lying on underlying subspaces. However, few of the existing low-rank regression methods can handle the outliers/noise lying on the sparsely corrupted disjoint subspaces. To address this issue, we propose a low-rank-sparse subspace representation for robust regression, hereafter referred to as LRS-RR in this paper. The main contribution include the following: (1) Unlike most of the existing regression methods, we propose an approach with two phases of low-rank-sparse subspace recovery and regression optimization being carried out simultaneously;(2) we also apply the linearized alternating direction method with adaptive penalty to solved the formulated LRS-RR problem and prove the convergence of the algorithm and analyze its complexity; (3) we demonstrate the efficiency of our method for the high-dimensional corrupted data on both synthetic data and two benchmark datasets against several state-of-the-art robust methods.

@InProceedings{Zhang_2017_CVPR,author = {Zhang, Yongqiang and Shi, Daming and Gao, Junbin and Cheng, Dansong},title = {Low-Rank-Sparse Subspace Representation for Robust Regression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}}


AGA: Attribute-Guided Augmentation

Mandar Dixit, Roland Kwitt, Marc Niethammer, Nuno Vasconcelos

Abstract: We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to a large external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that  learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.

@InProceedings{Dixit_2017_CVPR,author = {Dixit, Mandar and Kwitt, Roland and Niethammer, Marc and Vasconcelos, Nuno},title = {AGA: Attribute-Guided Augmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}}


Shuai Yang, Jiaying Liu, Zhouhui Lian, Zongming Guo

Abstract: In this work, we explore the problem of generating fantastic special-effects for the typography. It is quite challenging due to the model diversities to illustrate varied text effects for different characters. To address this issue, our key idea is to exploit the analytics on the high regularity of the spatial distribution for text effects to guide the synthesis process. Specifically, we characterize the stylized patches by their normalized positions and the optimal scales to depict their style elements. Our method first estimates these two features and derives their correlation statistically. They are then converted into soft constraints for texture transfer to accomplish adaptive multi-scale texture synthesis and to make style element distribution uniform. It allows our algorithm to produce artistic typography that fits for both local texture patterns and the global spatial distribution in the example. Experimental results demonstrate the superiority of our method for various text effects over conventional style transfer methods. In addition, we validate the effectiveness of our algorithm with extensive artistic typography library generation.

@InProceedings{Yang_2017_CVPR,author = {Yang, Shuai and Liu, Jiaying and Lian, Zhouhui and Guo, Zongming},title = {Awesome Typography: Statistics-Based Text Effects Transfer},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}}


Steve Branson, Grant Van Horn, Pietro Perona

Abstract: We introduce a method to greatly reduce the amount of redundant annotations required when crowdsourcing annotations such as bounding boxes, parts, and class labels. For example, if two Mechanical Turkers happen to click on the same pixel location when annotating a part in a given image--an event that is very unlikely to occur by random chance--, it is a strong indication that the location is correct. A similar type of confidence can be obtained if a single Turker happened to agree with a computer vision estimate. We thus incrementally collect a variable number of worker annotations per image based on online estimates of confidence. This is done using a sequential estimation of risk over a probabilistic model that combines worker skill, image difficulty, and an incrementally trained computer vision model. We develop specialized models and algorithms for binary annotation, part keypoint annotation, and sets of bounding box annotations. We show that our method can reduce annotation time by a factor of 4-11 for binary filtering of websearch results, 2-4 for annotation of boxes of pedestrians in images, while in many cases also reducing annotation error. We will make an end-to-end version of our system publicly available.

@InProceedings{Branson_2017_CVPR,author = {Branson, Steve and Van Horn, Grant and Perona, Pietro},title = {Lean Crowdsourcing: Combining Humans and Machines in an Online System},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}}