Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation

15 October 2021

Papers citing "Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation"

8 / 8 papers shown

Title
Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions Runhao Zeng Xiaoyong Chen Jiaming Liang Huisi Wu Guangzhong Cao Yong Guo AAML 36 3 0 29 Mar 2024
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers Haoyang Liu Aditya Singh Yijiang Li Haohan Wang AAML ViT 36 1 0 15 Mar 2024
Random Position Adversarial Patch for Vision Transformers Mingzhen Shao ViT AAML 30 2 0 09 Jul 2023
You Only Cut Once: Boosting Data Augmentation with a Single Cut Junlin Han Pengfei Fang Weihong Li Jie Hong M. Armin Ian Reid L. Petersson Hongdong Li 27 27 0 28 Jan 2022
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities Xin Du Bénédicte Legastelois B. Ganesh A. Rajan Hana Chockler Vaishak Belle Stuart Anderson S. Ramamoorthy AAML 21 6 0 27 Jan 2022
Intriguing Properties of Vision Transformers Muzammal Naseer Kanchana Ranasinghe Salman Khan Munawar Hayat F. Khan Ming-Hsuan Yang ViT 256 620 0 21 May 2021
On the Adversarial Robustness of Vision Transformers Rulin Shao Zhouxing Shi Jinfeng Yi Pin-Yu Chen Cho-Jui Hsieh ViT 27 137 0 29 Mar 2021
Negative Data Augmentation Abhishek Sinha Kumar Ayush Jiaming Song Burak Uzkent Hongxia Jin Stefano Ermon 39 72 0 09 Feb 2021