Awesome Causal Vision

A curated list of research papers in exploring causality in vision. Link to the code if available is also present. I might have missed some paper(s) or added some irrelevant paper(s). Feel free to open an issue in that case. I will go through the paper and then add / remove it.

Paper

1. Discovering causal signals in images. Lopez-Paz, D., Nishihara, R., Chintala, S., Scholkopf, B. and Bottou, L., 2017. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [Paper]

2. Causally regularized learning with agnostic data selection bias. Shen, Z., Cui, P., Kuang, K., Li, B. and Chen, P., 2018. Proceedings of the 26th ACM international conference on Multimedia. [Paper]

3. Causal reasoning from meta-reinforcement learning. Dasgupta, I., Wang, J., Chiappa, S., Mitrovic, J., Ortega, P., Raposo, D., Hughes, E., Battaglia, P., Botvinick, M. and Kurth-Nelson, Z., 2019. arXiv preprint arXiv:1901.08162. [Paper]

4. Unbiased scene graph generation from biased training. Tang, K., Niu, Y., Huang, J., Shi, J. and Zhang, H., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [Paper]

5. Visual commonsense r-cnn. Wang, T., Huang, J., Zhang, H. and Sun, Q., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [Paper]

6. Two causal principles for improving visual dialog. Qi, J., Niu, Y., Huang, J. and Zhang, H., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [Paper]

7. Counterfactual samples synthesizing for robust visual question answering. Chen, L., Yan, X., Xiao, J., Zhang, H., Pu, S. and Zhuang, Y., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [Paper]

8. Counterfactual VQA: A Cause-Effect Look at Language Bias. Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X.S. and Wen, J.R., 2020. arXiv preprint arXiv:2006.04315.[Paper]

9. Counterfactual vision and language learning. Abbasnejad, E., Teney, D., Parvaneh, A., Shi, J. and Hengel, A.V.D., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [Paper]

10. Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling. Fu, T.J., Wang, X., Peterson, M., Grafton, S., Eckstein, M. and Wang, W.Y., 2019. arXiv preprint arXiv:1911.07308. [Paper]

11. Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing. Agarwal, V., Shetty, R. and Fritz, M., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [Paper]

12. Robust Neural Network for Causal Invariant Features Extraction. Zeng, S., Zhang, P., Charles, D., Manavoglu, E., & Kiciman, E. 2019. NIPS workshop. [Paper]

13. DeVLBert: Learning Deconfounded Visio-Linguistic Representations. Zhang, S., Jiang, T., Wang, T., Kuang, K., Zhao, Z., Zhu, J., Yu, J., Yang, H. and Wu, F., 2020. arXiv preprint arXiv:2008.06884. [Paper]

Datasets

1. Clevrer: Collision events for video representation and reasoning. Yi, K., Gan, C., Li, Y., Kohli, P., Wu, J., Torralba, A. and Tenenbaum, J.B., 2019. arXiv preprint arXiv:1910.01442. [Paper] [Project]

Survey

1. A survey of learning causality with data: Problems and methods. Guo, R., Cheng, L., Li, J., Hahn, P.R. and Liu, H., 2020. ACM Computing Surveys (CSUR), 53(4), pp.1-37. [Paper]

2. Causal Inference. Kuang, K., Li, L., Geng, Z., Xu, L., Zhang, K., Liao, B., Huang, H., Ding, P., Miao, W. and Jiang, Z., 2020. Engineering, 6(3), pp.253-263. [Paper]

Groups

1. Microsoft Causality and Machine Learning Group [Link]

Causality Books

1. Interpretation and identification of causal mediation. Judea Pearl, 2014. pdf
2. (book) The Book of Why. Judea Pearl, 2018. [onedrive]
3. (book) The Book of Why(中文版). Judea Pearl & Dana Mackenzie, 江⽣ & 于华 译, 2018. [onedrive]
4. (book) Causality: Models, Reasoning, and Inference(2nd Edition). Judea Pearl, 2009. [onedrive]
5. (book) Causal inference in statistics: An overview. Judea Pearl, on Statistics Surveys, 2009. [onedrive]
6. (book) 因果推断简介. 丁鹏(北京大学). [onedrive]
7. (book) Causal Inference - What If. Miguel A. Hernán & James M. Robins, 2019. [onedrive]
8. (book) Elements of Causal Inference: Foundations and Learning Algorithms. MIT, 2020. [onedrive]
9. (book) Introduction to Causal Inference: from a Machine Learning Perspective. Brady Neal, Course Lecture Notes, 2020. [onedrive]

Causality PPT

1. KDD 2020 Tutorial - Causal Inference and Stable Learning. [ppt]
2. MLSS 2020 - Causility. [onedrive]
3. MLSS 2020 - Causal Inference II. [onedrive]