What If We Could Not See? Counterfactual Analysis for Egocentric Action Anticipation
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 1316-1322.
https://doi.org/10.24963/ijcai.2021/182
Keywords
- Computer Vision: Video: Events, Activities and Surveillance
Abstract
Egocentric action anticipation aims at predicting the near future based on past observation in first-person vision. While future actions may be wrongly predicted due to the dataset bias, we present a counterfactual analysis framework for egocentric action anticipation (CA-EAA) to enhance the capacity. In the factual case, we can predict the upcoming action based on visual features and semantic labels from past observation. Imagining one counterfactual situation where no visual representation had been observed, we would obtain a counterfactual predicted action only using past semantic labels. In this way, we can reduce the side-effect caused by semantic labels via a comparison between factual and counterfactual outcomes, which moves a step towards unbiased prediction for egocentric action anticipation. We conduct experiments on two large-scale egocentric video datasets. Qualitative and quantitative results validate the effectiveness of our proposed CA-EAA.
@inproceedings{ijcai2021p182,
title = {What If We Could Not See? Counterfactual Analysis for Egocentric Action Anticipation},
author = {Zhang, Tianyu and Min, Weiqing and Yang, Jiahao and Liu, Tao and Jiang, Shuqiang and Rui, Yong},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {1316--1322},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/182},
url = {https://doi.org/10.24963/ijcai.2021/182},
}
TY - CPAPER TI - What If We Could Not See? Counterfactual Analysis for Egocentric Action Anticipation AU - Zhang, Tianyu AU - Min, Weiqing AU - Yang, Jiahao AU - Liu, Tao AU - Jiang, Shuqiang AU - Rui, Yong T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 1316 EP - 1322 DO - 10.24963/ijcai.2021/182 UR - https://doi.org/10.24963/ijcai.2021/182 ER -Copied
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