/awesome-vqa

Visual Q&A reading list

MIT LicenseMIT

Awesome Visual Question Answering Awesome

A reading list of resources dedicated to visual(image/video) question answering.

Contributing

Please feel free to contact me. Issues and PRs are also welcome.

Papers

Review Papers

  • Latest Papers
    • Kushal Kafle, and Christopher Kanan. Visual question answering: Datasets, algorithms, and future challenges. Computer Vision and Image Understanding (2017). [Paper]
    • Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony Dick, and Anton van den Hengel. Visual question answering: A survey of methods and datasets. Computer Vision and Image Understanding (2017). [Paper]

CLEVR QA

  • Latest Papers
    • Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick, CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning, CVPR 2017. [Paper]
    • Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick, Inferring and Executing Programs for Visual Reasoning, arXiv:1705.03633, 2017. [Paper]
    • Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Kate Saenko, Learning to Reason: End-to-End Module Networks for Visual Question Answering, arXiv:1704.05526, 2017. [Paper]
    • Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap, A simple neural network module for relational reasoning, arXiv:1706.01427, 2017. [Paper]

Image QA

  • Latest Papers

    • Yan Zhang, Jonathon Hare, Adam Prügel-Bennett: Learning to Count Objects in Natural Images for Visual Question Answering [Paper] [Code]
    • Hedi Ben-younes, Remi Cadene, Matthieu Cord, Nicolas Thome: MUTAN: Multimodal Tucker Fusion for Visual Question Answering [Paper] [Code]
    • Vahid Kazemi, Ali Elqursh, Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering, arXiv:1704.03162, 2016. [Paper] [Code]
    • Kushal Kafle, and Christopher Kanan. An Analysis of Visual Question Answering Algorithms. arXiv:1703.09684, 2017. [Paper] [website]
    • Hyeonseob Nam, Jung-Woo Ha, Jeonghee Kim, Dual Attention Networks for Multimodal Reasoning and Matching, arXiv:1611.00471, 2016. [Paper]
    • Jin-Hwa Kim, Kyoung Woon On, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang, Hadamard Product for Low-rank Bilinear Pooling, arXiv:1610.04325, 2016. [Paper]
    • Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, Marcus Rohrbach, Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, arXiv:1606.01847, 2016. [Paper] [code]
    • Kuniaki Saito, Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada, DualNet: Domain-Invariant Network for Visual Question Answering. arXiv:1606.06108v1, 2016. [Paper]
    • Arijit Ray, Gordon Christie, Mohit Bansal, Dhruv Batra, Devi Parikh, Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions, arXiv:1606.06622, 2016. [Paper]
    • Hyeonwoo Noh, Bohyung Han, Training Recurrent Answering Units with Joint Loss Minimization for VQA, arXiv:1606.03647, 2016. [Paper]
    • Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh, Hierarchical Question-Image Co-Attention for Visual Question Answering, arXiv:1606.00061, 2016. [Paper] [code]
    • Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang, Multimodal Residual Learning for Visual QA, arXiv:1606.01455, 2016. [Paper]
    • Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel, Anthony Dick, FVQA: Fact-based Visual Question Answering, arXiv:1606.05433, 2016. [Paper]
    • Ilija Ilievski, Shuicheng Yan, Jiashi Feng, A Focused Dynamic Attention Model for Visual Question Answering, arXiv:1604.01485. [Paper]
    • Yuke Zhu, Oliver Groth, Michael Bernstein, Li Fei-Fei, Visual7W: Grounded Question Answering in Images, CVPR 2016. [Paper]
    • Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han, Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction, CVPR, 2016.[Paper]
    • Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, Learning to Compose Neural Networks for Question Answering, NAACL 2016. [Paper]
    • Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, Deep compositional question answering with neural module networks, CVPR 2016. [Paper]
    • Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, Stacked Attention Networks for Image Question Answering, CVPR 2016. [Paper] [code]
    • Kevin J. Shih, Saurabh Singh, Derek Hoiem, Where To Look: Focus Regions for Visual Question Answering, CVPR, 2015. [Paper]
    • Kan Chen, Jiang Wang, Liang-Chieh Chen, Haoyuan Gao, Wei Xu, Ram Nevatia, ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering, arXiv:1511.05960v1, Nov 2015. [Paper]
    • Huijuan Xu, Kate Saenko, Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering, arXiv:1511.05234v1, Nov 2015. [Paper]
    • Kushal Kafle and Christopher Kanan, Answer-Type Prediction for Visual Question Answering, CVPR 2016. [Paper]
    • Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, ICCV, 2015. [Paper] [code]
    • Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus, Simple Baseline for Visual Question Answering, arXiv:1512.02167v2, Dec 2015. [Paper]
    • Hauyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu, Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering, NIPS 2015. [Paper]
    • Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, Ask Your Neurons: A Neural-based Approach to Answering Questions about Images, ICCV 2015. [Paper]
    • Mengye Ren, Ryan Kiros, Richard Zemel, Exploring Models and Data for Image Question Answering, ICML 2015. [Paper]
    • Mateusz Malinowski, Mario Fritz, Towards a Visual Turing Challe, NIPS Workshop 2015. [Paper]
    • Mateusz Malinowski, Mario Fritz, A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input, NIPS 2014. [Paper]

  • Attention-Based

    • Hedi Ben-younes, Remi Cadene, Matthieu Cord, Nicolas Thome: MUTAN: Multimodal Tucker Fusion for Visual Question Answering [Paper] [Code]
    • Jin-Hwa Kim, Kyoung Woon On, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang, Hadamard Product for Low-rank Bilinear Pooling, arXiv:1610.04325, 2016. [Paper]
    • Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, Marcus Rohrbach, Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, arXiv:1606.01847, 2016. [Paper]
    • Hyeonwoo Noh, Bohyung Han, Training Recurrent Answering Units with Joint Loss Minimization for VQA, arXiv:1606.03647, 2016. [Paper]
    • Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh, Hierarchical Question-Image Co-Attention for Visual Question Answering, arXiv:1606.00061, 2016. [Paper]
    • Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, Stacked Attention Networks for Image Question Answering, CVPR 2016. [Paper]
    • Ilija Ilievski, Shuicheng Yan, Jiashi Feng, A Focused Dynamic Attention Model for Visual Question Answering, arXiv:1604.01485. [Paper]
    • Kan Chen, Jiang Wang, Liang-Chieh Chen, Haoyuan Gao, Wei Xu, Ram Nevatia, ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering, arXiv:1511.05960v1, Nov 2015. [Paper]
    • Huijuan Xu, Kate Saenko, Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering, arXiv:1511.05234v1, Nov 2015. [Paper]

  • Knowledge-based

    • Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel, Anthony Dick, FVQA: Fact-based Visual Question Answering, arXiv:1606.05433, 2016. [Paper]
    • Qi Wu, Peng Wang, Chunhua Shen, Anton van den Hengel, Anthony Dick, Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources, CVPR 2016. [Paper]
    • Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel, Anthony Dick, Explicit Knowledge-based Reasoning for Visual Question Answering, arXiv:1511.02570v2, Nov 2015. [Paper]
    • Yuke Zhu, Ce Zhang, Christopher Re,́ Li Fei-Fei, Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries, arXiv:1507.05670, Nov 2015. [Paper]

  • Memory Network

    • Caiming Xiong, Stephen Merity, Richard Socher, Dynamic Memory Networks for Visual and Textual Question Answering, ICML 2016. [Paper]
    • Aiwen Jiang, Fang Wang, Fatih Porikli, Yi Li, Compositional Memory for Visual Question Answering, arXiv:1511.05676v1, Nov 2015. [Paper]

Video QA

  • Kuo-Hao Zeng, Tseng-Hung Chen, Ching-Yao Chuang, Yuan-Hong Liao, Juan Carlos Niebles, Min Sun, Leveraging Video Descriptions to Learn Video Question Answering, AAAI 2017. [paper]
  • Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, Sanja Fidler, MovieQA: Understanding Stories in Movies through Question-Answering, CVPR 2016. [Paper]
  • Linchao Zhu, Zhongwen Xu, Yi Yang, Alexander G. Hauptmann, Uncovering Temporal Context for Video Question and Answering, arXiv:1511.05676v1, Nov 2015. [Paper]