{"type":"ScholarlyArticle","title":"Improve Video Representation with Temporal Adversarial Augmentation","authors":[{"name":"Jinhao Duan","slug":"jinhao-duan","given":"Jinhao","family":"Duan","affiliation":"Drexel University"},{"name":"Quanfu Fan","slug":"quanfu-fan","given":"Quanfu","family":"Fan","affiliation":"Amazon"},{"name":"Hao Cheng","slug":"hao-cheng","given":"Hao","family":"Cheng","affiliation":"The Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Xiaoshuang Shi","slug":"xiaoshuang-shi","given":"Xiaoshuang","family":"Shi","affiliation":"University of Electronic Science and Technology of China"},{"name":"Kaidi Xu","slug":"kaidi-xu","given":"Kaidi","family":"Xu","affiliation":"Drexel University"}],"abstract":"Recent works reveal that adversarial augmentation benefits the generalization of neural networks (NNs) if used in an appropriate manner. In this paper, we introduce Temporal Adversarial Augmentation (TA), a novel video augmentation technique that utilizes temporal attention. Unlike conventional adversarial augmentation, TA is specifically designed to shift the attention distributions of neural networks with respect to video clips by maximizing a temporal-related loss function. We demonstrate that TA will obtain diverse temporal views, which significantly affect the focus of neural networks. Training with these examples remedies the flaw of unbalanced temporal information perception and enhances the ability to defend against temporal shifts, ultimately leading to better generalization. To leverage TA, we propose Temporal Video Adversarial Fine-tuning (TAF) framework for improving video representations. TAF is a model-agnostic, generic, and interpretability-friendly training strategy. We evaluate TAF with four powerful models (TSM, GST, TAM, and TPN) over three challenging temporal-related benchmarks (Something-something V1&V2 and diving48). Experimental results demonstrate that TAF effectively improves the test accuracy of these models with notable margins without introducing additional parameters or computational costs. As a byproduct, TAF also improves the robustness under out-of-distribution (OOD) settings. Code is available at https://github.com/jinhaoduan/TAF.","doi":"10.24963/ijcai.2023/79","url":"https://ijcai.org/proceedings/2023/79","pdf":"https://ijcai.org/proceedings/2023/0079.pdf","markdown":"https://ijcai.org/proceedings/2023/79.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/79","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":79,"pages":{"first":708,"last":716},"keywords":["Computer Vision: CV: Adversarial learning, adversarial attack and defense methods","Computer Vision: CV: Representation learning","Computer Vision: CV: Video analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Arnab et al., 2021] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. 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