Optimizing Relevance Maps of Vision Transformers Improves Robustness

Optimizing Relevance Maps of Vision Transformers Improves Robustness

2 June 2022

Lior Wolf

Papers citing "Optimizing Relevance Maps of Vision Transformers Improves Robustness"

7 / 7 papers shown

Title
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers Haoyang Liu Aditya Singh Yijiang Li Haohan Wang AAML ViT 24 1 0 15 Mar 2024
Attention-aware Semantic Communications for Collaborative Inference Jiwoong Im Nayoung Kwon Taewoo Park Jiheon Woo Jaeho Lee Yongjune Kim 20 2 0 23 Feb 2024
Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models Huy Ha Shuran Song LM&Ro VLM 14 101 0 23 Jul 2022
Rethinking Attention-Model Explainability through Faithfulness Violation Test Y. Liu Haoliang Li Yangyang Guo Chen Kong Jing Li Shiqi Wang FAtt 113 41 0 28 Jan 2022
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 283 5,723 0 29 Apr 2021
Counterfactual Samples Synthesizing for Robust Visual Question Answering Long Chen Xin Yan Jun Xiao Hanwang Zhang Shiliang Pu Yueting Zhuang OOD AAML 123 287 0 14 Mar 2020
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 279 39,083 0 01 Sep 2014