视觉问答VQA知识资料全集

本文主要是介绍视觉问答VQA知识资料全集，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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
- https://mp.weixin.qq.com/s/dyor9bv2y0VyX7woMDVLkA
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]