Interpretable counting for visual question answering

6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings

Volumn , Issue , 2018, Pages

  Trott, Alexander ^a   Xiong, Caiming ^a   Socher, Richard ^a  

^a Salesforcecom Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRETE CHOICE; QUESTION ANSWERING; SEQUENTIAL DECISION PROCESS; STATE OF THE ART;

OBJECT DETECTION;

EID: 85083952592     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (48)

1. Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Devi Parikh, and Dhruv Batra. VQA: Visual Question Answering. International Journal of Computer Vision, 2015.
2. Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. Bottom-Up and Top-Down Attention for Image Captioning and VQA. arXiv, 2017.
3. Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. Neural module networks. In CVPR, 2016a.
4. Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. Learning to Compose Neural Networks for Question Answering. In NAACL, 2016b.
5. Arjun Chandrasekaran, Deshraj Yadav, Prithvijit Chattopadhyay, Viraj Prabhu, and Devi Parikh. It Takes Two to Tango: Towards Theory of AI’s Mind. arXiv, 2017.
6. Prithvijit Chattopadhyay, Ramakrishna Vedantam, Ramprasaath R. Selvaraju, Dhruv Batra, and Devi Parikh. Counting Everyday Objects in Everyday Scenes. In CVPR, 2017.
7. Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. R-FCN: Object Detection via Region-based Fully Convolutional Networks. In NIPS, 2016.
8. Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach. Multimodal compact bilinear pooling for visual question answering and visual grounding. In EMNLP, 2016.
9. Ross Girshick. Fast R-CNN. In ICCV, 2015.
10. Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2015.
11. Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering. In CVPR, 2017.
12. Sepp Hochreiter and Jürgen Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8): 1735–1780, 1997.
13. Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko. Learning to Reason: End-to-End Module Networks for Visual Question Answering. In ICCV, 2017.
14. Ilija Ilievski, Shuicheng Yan, and Jiashi Feng. A Focused Dynamic Attention Model for Visual Question Answering. arXiv, 2016.
15. Jin-Hwa Kim, Kyoung-Woon On, Woosang Lim, Jeonghee Kim, Jung-Woo Ha, and Byoung-Tak Zhang. Hadamard Product for Low-rank Bilinear Pooling. In ICLR, 2017.
16. Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. arXiv, 2014.
17. Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Fei-Fei Li. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations. International Journal of Computer Vision, 2016.
18. Victor Lempitsky and Andrew Zisserman. Learning To Count Objects in Images. NIPS, 2010.
19. Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. Microsoft COCO: Common Objects in Context. In ECCV, 2014.
20. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal Loss for Dense Object Detection. ICCV, 2017.
21. Jiasen Lu, Caiming Xiong, Devi Parikh, and Richard Socher. Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning. In CVPR, 2016a.
22. Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. Hierarchical Question-Image Co-Attention for Visual Question Answering. In NIPS, 2016b.
23. Yuping Luo, Chung-cheng Chiu, Navdeep Jaitly, and Ilya Sutskever. Learning Online Alignments with Continuous Rewards Policy Gradient. In ICASSP, 2017.
24. Mateusz Malinowski and Mario Fritz. A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input. In NIPS, 2014.
25. Volodymyr Minh, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Tim Harley, Timothy Lillicrap, David Silver, and Koray Kavukcuoglu. Asynchronous Methods for Deep Reinforcement Learning. In ICML, 2016.
26. Daniel Oñoro-Rubio and Roberto J. López-Sastre. Towards perspective-free object counting with deep learning. In ECCV, 2016.
27. Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach. Attentive Explanations: Justifying Decisions and Pointing to the Evidence. arXiv, 2016.
28. Romain Paulus, Caiming Xiong, and Richard Socher. A Deep Reinforced Model for Abstractive Summarization. arXiv, 2017.
29. Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global Vectors for Word Representation. EMNLP, 2014.
30. Mengye Ren and Richard S. Zemel. End-to-End Instance Segmentation with Recurrent Attention. In CVPR, 2017.
31. Mengye Ren, Ryan Kiros, and Richard Zemel. Exploring Models and Data for Image Question Answering. In NIPS, 2015a.
32. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In NIPS, 2015b.
33. Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, and Vaibhava Goel. Self-critical Sequence Training for Image Captioning. In CVPR, 2017.
34. Santi Segui, Oriol Pujol, and Jordi Vitria. Learning to count with deep object features. In CVPRW, 2015.
35. Kevin J. Shih, Saurabh Singh, and Derek Hoiem. Where To Look: Focus Regions for Visual Question Answering. In CVPR, 2015.
36. Richard Socher, Andrej Karpathy, Quoc V Le, Christopher D Manning, and Andrew Y Ng. Grounded Compositional Semantics for Finding and Describing Images with Sentences. In TACL, 2014.
37. Damien Teney, Lingqiao Liu, and Anton van den Hengel. Graph-Structured Representations for Visual Question Answering. arXiv, 2016.
38. Damien Teney, Peter Anderson, Xiaodong He, and Anton van den Hengel. Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge. In CVPR, 2017.
39. Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. Conditional Image Generation with PixelCNN Decoders. In NIPS, 2016.
40. R J Williams. Simple statistical gradient-following methods for connectionist reinforcement learning. Machine Learning, 8:229–256, 1992.
41. Ronald J. Williams and Jing Peng. Function Optimization using Connectionist Reinforcement Learning Algorithms. Connection Science, 3(3):241–268, 1991.
42. Caiming Xiong, Stephen Merity, and Richard Socher. Dynamic Memory Networks for Visual and Textual Question Answering. In ICML, 2016.
43. Huijuan Xu and Kate Saenko. Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering. In ECCV, 2015.
44. Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola. Stacked Attention Networks for Image Question Answering. In CVPR, 2015.
45. Cong Zhang, Hongsheng Li, Xiaogang Wang, and Xiaokang Yang. Cross-scene crowd counting via deep convolutional neural networks. In CVPR, 2015.
46. Jianming Zhang, Shugao Ma, Mehrnoosh Sameki, Stan Sclaroff, Margrit Betke, Zhe Lin, Xiao-hui Shen, Brian Price, and Radomír Měch. Salient Object Subitizing. International Journal of Computer Vision, 2017.
47. Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus. Simple Baseline for Visual Question Answering. arXiv, 2015.
48. Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7W: Grounded Question Answering in Images. In CVPR, 2015.