Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution Shifts

21 January 2024

Papers citing "Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution Shifts"

7 / 7 papers shown

Title
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training Junhao Dong Seyed-Mohsen Moosavi-Dezfooli Jianhuang Lai Xiaohua Xie AAML 32 26 0 01 Nov 2022
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition Kiyoon Kim Shreyank N. Gowda Oisin Mac Aodha Laura Sevilla-Lara 22 9 0 25 Jan 2022
Recent Advances in Adversarial Training for Adversarial Robustness Tao Bai Jinqi Luo Jun Zhao B. Wen Qian Wang AAML 71 467 0 02 Feb 2021
MVFNet: Multi-View Fusion Network for Efficient Video Recognition Wenhao Wu Dongliang He Tianwei Lin Fu Li Chuang Gan Errui Ding 87 68 0 13 Dec 2020
Deep Domain-Adversarial Image Generation for Domain Generalisation Kaiyang Zhou Yongxin Yang Timothy M. Hospedales Tao Xiang OOD 201 402 0 12 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 45 63 0 02 Mar 2020
AdaFrame: Adaptive Frame Selection for Fast Video Recognition Zuxuan Wu Caiming Xiong Chih-Yao Ma R. Socher L. Davis 113 194 0 29 Nov 2018