【专知荟萃21】视觉问答VQA知识资料全集(入门/进阶/论文/综述/视频/专家,附查看)

2017 年 11 月 20 日 专知 专知内容组

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    视觉问答(Visual Question Answering,VQA)专知荟萃

      • 入门学习

      • 进阶论文

        • Attention-Based

        • Knowledge-based

        • Memory Network

        • Video QA

      • 综述

      • Tutorial

      • Dataset

      • Code

      • 领域专家



入门学习

  • 基于深度学习的VQA(视觉问答)技术

    • [https://zhuanlan.zhihu.com/p/22530291]

  • 视觉问答全景概述:从数据集到技术方法

  • 论文读书笔记(Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding)

    • [http://www.jianshu.com/p/5bf03d1fadfa]

  • 能看图回答问题的AI离我们还有多远?Facebook向视觉对话进发

    • [https://www.leiphone.com/news/201711/4B9cNlCINsVyPdTw.html]

  • 图像问答Image Question Answering

    • [http://www.cnblogs.com/ranjiewen/p/7604468.html]

  • 实战深度学习之图像问答

    • [https://zhuanlan.zhihu.com/p/20899091]

  • 2017 VQA Challenge 第一名技术报告

    • [https://zhuanlan.zhihu.com/p/29688475]

  • 深度学习为视觉和语言之间搭建了一座桥梁

    • [http://www.msra.cn/zh-cn/news/features/vision-and-language-20170713]


进阶论文

  • Kushal Kafle, and Christopher Kanan. Visual question answering: Datasets, algorithms, and future challenges. Computer Vision and Image Understanding [2017].

    • [https://arxiv.org/abs/1610.01465]

  • 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.

    • [http://vision.stanford.edu/pdf/johnson2017cvpr.pdf]

  • 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. [https://arxiv.org/abs/1705.03633]

  • 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. [https://arxiv.org/abs/1704.05526]

  • 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. [https://arxiv.org/abs/1706.01427]

  • Hedi Ben-younes, Remi Cadene, Matthieu Cord, Nicolas Thome: MUTAN: Multimodal Tucker Fusion for Visual Question Answering [https://arxiv.org/pdf/1705.06676.pdf] [https://github.com/Cadene/vqa.pytorch]

  • Vahid Kazemi, Ali Elqursh, Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering, arXiv:1704.03162, 2016. [https://arxiv.org/abs/1704.03162] [https://github.com/Cyanogenoid/pytorch-vqa]

  • Kushal Kafle, and Christopher Kanan. An Analysis of Visual Question Answering Algorithms. arXiv:1703.09684, 2017. [https://arxiv.org/abs/1703.09684]

  • Hyeonseob Nam, Jung-Woo Ha, Jeonghee Kim, Dual Attention Networks for Multimodal Reasoning and Matching, arXiv:1611.00471, 2016. [https://arxiv.org/abs/1611.00471]

  • 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. [https://arxiv.org/abs/1610.04325]

  • 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. [https://arxiv.org/abs/1606.01847] [https://github.com/akirafukui/vqa-mcb]

  • Kuniaki Saito, Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada, DualNet: Domain-Invariant Network for Visual Question Answering. arXiv:1606.06108v1, 2016. [https://arxiv.org/pdf/1606.06108.pdf]

  • 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. [https://arxiv.org/pdf/1606.06622v1.pdf]

  • Hyeonwoo Noh, Bohyung Han, Training Recurrent Answering Units with Joint Loss Minimization for VQA, arXiv:1606.03647, 2016. [http://arxiv.org/abs/1606.03647v1]

  • Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh, Hierarchical Question-Image Co-Attention for Visual Question Answering, arXiv:1606.00061, 2016. [https://arxiv.org/pdf/1606.00061v2.pdf] [https://github.com/jiasenlu/HieCoAttenVQA]

  • 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. [https://arxiv.org/pdf/1606.01455v1.pdf]

  • Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel, Anthony Dick, FVQA: Fact-based Visual Question Answering, arXiv:1606.05433, 2016. [https://arxiv.org/pdf/1606.05433.pdf]

  • Ilija Ilievski, Shuicheng Yan, Jiashi Feng, A Focused Dynamic Attention Model for Visual Question Answering, arXiv:1604.01485. [https://arxiv.org/pdf/1604.01485v1.pdf]

  • Yuke Zhu, Oliver Groth, Michael Bernstein, Li Fei-Fei, Visual7W: Grounded Question Answering in Images, CVPR 2016. [http://arxiv.org/abs/1511.03416]

  • Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han, Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction, CVPR, 2016.[http://arxiv.org/pdf/1511.05756.pdf]

  • Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, Learning to Compose Neural Networks for Question Answering, NAACL 2016. [http://arxiv.org/pdf/1601.01705.pdf]

  • Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, Deep compositional question answering with neural module networks, CVPR 2016. [https://arxiv.org/abs/1511.02799]

  • Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, Stacked Attention Networks for Image Question Answering, CVPR 2016. [http://arxiv.org/abs/1511.02274] [https://github.com/JamesChuanggg/san-torch]

  • Kevin J. Shih, Saurabh Singh, Derek Hoiem, Where To Look: Focus Regions for Visual Question Answering, CVPR, 2015. [http://arxiv.org/pdf/1511.07394v2.pdf]

  • 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. [http://arxiv.org/pdf/1511.05960v1.pdf]

  • Huijuan Xu, Kate Saenko, Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering, arXiv:1511.05234v1, Nov 2015. [http://arxiv.org/abs/1511.05234]

  • Kushal Kafle and Christopher Kanan, Answer-Type Prediction for Visual Question Answering, CVPR 2016. [http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Kafle_Answer-Type_Prediction_for_CVPR_2016_paper.html]

  • Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, ICCV, 2015. [http://arxiv.org/pdf/1505.00468]

  • Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, ICCV, 2015. [http://arxiv.org/pdf/1505.00468] [https://github.com/JamesChuanggg/VQA-tensorflow]

  • Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus, Simple Baseline for Visual Question Answering, arXiv:1512.02167v2, Dec 2015. [http://arxiv.org/abs/1512.02167]

  • 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. [http://arxiv.org/pdf/1505.05612.pdf]

  • Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, Ask Your Neurons: A Neural-based Approach to Answering Questions about Images, ICCV 2015. [http://arxiv.org/pdf/1505.01121v3.pdf]

  • Mengye Ren, Ryan Kiros, Richard Zemel, Exploring Models and Data for Image Question Answering, ICML 2015. [http://arxiv.org/pdf/1505.02074.pdf]

  • Mateusz Malinowski, Mario Fritz, Towards a Visual Turing Challe, NIPS Workshop 2015. [http://arxiv.org/abs/1410.8027]

  • Mateusz Malinowski, Mario Fritz, A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input, NIPS 2014. [http://arxiv.org/pdf/1410.0210v4.pdf]


Attention-Based

  • Hedi Ben-younes, Remi Cadene, Matthieu Cord, Nicolas Thome: MUTAN: Multimodal Tucker Fusion for Visual Question Answering [https://arxiv.org/pdf/1705.06676.pdf] [https://github.com/Cadene/vqa.pytorch]

  • 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. [https://arxiv.org/abs/1610.04325]

  • 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. [https://arxiv.org/abs/1606.01847]

  • Hyeonwoo Noh, Bohyung Han, Training Recurrent Answering Units with Joint Loss Minimization for VQA, arXiv:1606.03647, 2016. [http://arxiv.org/abs/1606.03647v1]

  • Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh, Hierarchical Question-Image Co-Attention for Visual Question Answering, arXiv:1606.00061, 2016. [https://arxiv.org/pdf/1606.00061v2.pdf]

  • Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, Stacked Attention Networks for Image Question Answering, CVPR 2016. [http://arxiv.org/abs/1511.02274]

  • Ilija Ilievski, Shuicheng Yan, Jiashi Feng, A Focused Dynamic Attention Model for Visual Question Answering, arXiv:1604.01485. [https://arxiv.org/pdf/1604.01485v1.pdf]

  • 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. [http://arxiv.org/pdf/1511.05960v1.pdf]

  • Huijuan Xu, Kate Saenko, Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering, arXiv:1511.05234v1, Nov 2015. [http://arxiv.org/abs/1511.05234]


Knowledge-based

  • Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel, Anthony Dick, FVQA: Fact-based Visual Question Answering, arXiv:1606.05433, 2016. [https://arxiv.org/pdf/1606.05433.pdf]

  • 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. [http://arxiv.org/abs/1511.06973]

  • 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. [http://arxiv.org/abs/1511.02570]

  • 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. [http://arxiv.org/abs/1507.05670]


Memory Network

  • Caiming Xiong, Stephen Merity, Richard Socher, Dynamic Memory Networks for Visual and Textual Question Answering, ICML 2016. [http://arxiv.org/abs/1603.01417]

  • Aiwen Jiang, Fang Wang, Fatih Porikli, Yi Li, Compositional Memory for Visual Question Answering, arXiv:1511.05676v1, Nov 2015. [http://arxiv.org/abs/1511.05676]


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. [https://arxiv.org/abs/1611.04021]

    • Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, Sanja Fidler, MovieQA: Understanding Stories in Movies through Question-Answering, CVPR 2016. [http://arxiv.org/abs/1512.02902]

    • Linchao Zhu, Zhongwen Xu, Yi Yang, Alexander G. Hauptmann, Uncovering Temporal Context for Video Question and Answering, arXiv:1511.05676v1, Nov 2015. [http://arxiv.org/abs/1511.04670]


综述

  • 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].

    • [https://arxiv.org/abs/1607.05910]

  • Tutorial on Answering Questions about Images with Deep Learning Mateusz Malinowski, Mario Fritz

    • [https://arxiv.org/abs/1610.01076]

  • Survey of Visual Question Answering: Datasets and Techniques

    • [https://arxiv.org/abs/1705.03865]

  • Visual Question Answering: Datasets, Algorithms, and Future Challenges

    • [https://arxiv.org/abs/1610.01465]


Tutorial

  • CVPR 2017 VQA Challenge Workshop  (有很多PPT)

    • [http://www.visualqa.org/workshop.html]

  • CVPR 2016 VQA Challenge Workshop 

    • [http://www.visualqa.org/vqa_v1_workshop.html\]

  • Tutorial on Answering Questions about Images with Deep Learning

    • [https://arxiv.org/pdf/1610.01076.pdf]

  • Visual Question Answering Demo in Python Notebook

    • [http://iamaaditya.github.io/2016/04/visual_question_answering_demo_notebook\]

  • Tutorial on Question Answering about Images

    • [https://www.linkedin.com/pulse/tutorial-question-answering-images-mateusz-malinowski/]


Dataset

  • Visual7W: Grounded Question Answering in Images

    • homepage: http://web.stanford.edu/~yukez/visual7w/

    • github: https://github.com/yukezhu/visual7w-toolkit

    • github: https://github.com/yukezhu/visual7w-qa-models

  • DAQUAR

    • [http://www.cs.toronto.edu/~mren/imageqa/results/\]

  • COCO-QA

    • [http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/\]

  • The VQA Dataset

    • [http://visualqa.org/]

  • FM-IQA

    • [http://idl.baidu.com/FM-IQA.html]

  • Visual Genome

    • [http://visualgenome.org/]


Code

  • VQA Demo: Visual Question Answering Demo on pretrained model

    • [https://github.com/iamaaditya/VQA_Demo]

    • [http://iamaaditya.github.io/research/]

  • deep-qa: Implementation of the Convolution Neural Network for factoid QA on the answer sentence selection task

    • [https://github.com/aseveryn/deep-qa]

  • YodaQA: A Question Answering system built on top of the Apache UIMA framework

    • [http://ailao.eu/yodaqa/]

    • [https://github.com/brmson/yodaqa]

  • insuranceQA-cnn-lstm: tensorflow and theano cnn code for insurance QA

    • [https://github.com/white127/insuranceQA-cnn-lstm]

  • Tensorflow Implementation of Deeper LSTM+ normalized CNN for Visual Question Answering

    • [https://github.com/JamesChuanggg/VQA-tensorflow]

  • Visual Question Answering with Keras

    • [https://anantzoid.github.io/VQA-Keras-Visual-Question-Answering/]

  • Deep Learning Models for Question Answering with Keras

    • [http://sujitpal.blogspot.jp/2016/10/deep-learning-models-for-question.html]

  • Deep QA: Using deep learning to answer Aristo's science questions

    • [https://github.com/allenai/deep_qa]

  • Visual Question Answering in Pytorch

    • [https://github.com/Cadene/vqa.pytorch]


领域专家

  • Qi Wu

    • [https://researchers.adelaide.edu.au/profile/qi.wu01]

  • Bolei Zhou 周博磊

    • [http://people.csail.mit.edu/bzhou/]

  • Stanislaw Antol

    • [https://computing.ece.vt.edu/~santol/\]

  • Jin-Hwa Kim

    • [https://bi.snu.ac.kr/~jhkim/\]

  • Vahid Kazemi

    • [http://www.csc.kth.se/~vahidk/index.html\]

  • Justin Johnson

    • [http://cs.stanford.edu/people/jcjohns/]

  • Ilija Ilievski

    • [https://ilija139.github.io/]




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