|
|
| --- |
| license: mit |
| language: |
| - vie |
| pretty_name: Openvivqa |
| task_categories: |
| - visual-question-answering |
| tags: |
| - visual-question-answering |
| --- |
| |
| OpenViVQA (Open-domain Vietnamese Visual Question Answering) is a dataset for VQA (Visual Question Answering) with |
| open-ended answers in Vietnamese. It consisted of 11199 images associated with 37914 question-answer pairs (QAs). |
| Images in the OpenViVQA dataset are captured in Vietnam and question-answer pairs are created manually by Vietnamese |
| crowd workers. |
|
|
|
|
| ## Languages |
|
|
| vie |
|
|
| ## Supported Tasks |
|
|
| Visual Question Answering |
|
|
| ## Dataset Usage |
| ### Using `datasets` library |
| ``` |
| from datasets import load_dataset |
| dset = datasets.load_dataset("SEACrowd/openvivqa", trust_remote_code=True) |
| ``` |
| ### Using `seacrowd` library |
| ```import seacrowd as sc |
| # Load the dataset using the default config |
| dset = sc.load_dataset("openvivqa", schema="seacrowd") |
| # Check all available subsets (config names) of the dataset |
| print(sc.available_config_names("openvivqa")) |
| # Load the dataset using a specific config |
| dset = sc.load_dataset_by_config_name(config_name="<config_name>") |
| ``` |
|
|
| More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use). |
|
|
|
|
| ## Dataset Homepage |
|
|
| [https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset](https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset) |
|
|
| ## Dataset Version |
|
|
| Source: 1.0.0. SEACrowd: 2024.06.20. |
|
|
| ## Dataset License |
|
|
| MIT (mit) |
|
|
| ## Citation |
|
|
| If you are using the **Openvivqa** dataloader in your work, please cite the following: |
| ``` |
| @article{NGUYEN2023101868, |
| title = {OpenViVQA: Task, dataset, and multimodal fusion models for visual question answering in Vietnamese}, |
| journal = {Information Fusion}, |
| volume = {100}, |
| pages = {101868}, |
| year = {2023}, |
| issn = {1566-2535}, |
| doi = {https://doi.org/10.1016/j.inffus.2023.101868}, |
| url = {https://www.sciencedirect.com/science/article/pii/S1566253523001847}, |
| author = {Nghia Hieu Nguyen and Duong T.D. Vo and Kiet {Van Nguyen} and Ngan Luu-Thuy Nguyen}, |
| keywords = {Visual question answering, Vision-language understanding, Low-resource languages, Information fusion, Multimodal representation}, |
| abstract = {In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers. Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English. However, available datasets narrow the VQA task as the answers selection task or answer classification task. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question–answer pairs (QAs). Moreover, we proposed FST, QuMLAG, and MLPAG which fuse information from images and questions, then use these fused features to construct answers as humans iteratively. Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C. The dataset11https://github.com/hieunghia-pat/OpenViVQA-dataset. is available to encourage the research community to develop more generalized algorithms including transformers for low-resource languages such as Vietnamese.} |
| } |
| |
| |
| @article{lovenia2024seacrowd, |
| title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, |
| author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, |
| year={2024}, |
| eprint={2406.10118}, |
| journal={arXiv preprint arXiv: 2406.10118} |
| } |
| |
| ``` |
