Papers
90 papers found
Are Vision-Language Transformers Learning Multimodal Representations? A Probing Perspective
Emmanuelle Salin, Badreddine Farah, Stéphane Ayache et al.
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers
Estelle Aflalo, Meng Du, Shao-Yen Tseng et al.
Weakly Supervised Grounding for VQA in Vision-Language Transformers
Aisha Urooj, Hilde Kuehne, Chuang Gan et al.
An Empirical Study of Training End-to-End Vision-and-Language Transformers
Zi-Yi Dou, Yichong Xu, Zhe Gan et al.
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
Mitja Nikolaus, Emmanuelle Salin, Stephane Ayache et al.
SegViT: Semantic Segmentation with Plain Vision Transformers
Bowen Zhang, Zhi Tian, Quan Tang et al.
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
Zhiwei Hao, Jianyuan Guo, Ding Jia et al.
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
Yucheng Shi, Yahong Han, Yu-an Tan et al.
EfficientFormer: Vision Transformers at MobileNet Speed
Yanyu Li, Geng Yuan, Yang Wen et al.
Fast Vision Transformers with HiLo Attention
Zizheng Pan, Jianfei Cai, Bohan Zhuang
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets
Zhiying Lu, Hongtao Xie, Chuanbin Liu et al.
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
Yao Qin, Chiyuan Zhang, Ting Chen et al.
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Shoufa Chen, Chongjian GE, Zhan Tong et al.
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Yichuan Mo, Dongxian Wu, Yifei Wang et al.
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
Viraj Prabhu, Sriram Yenamandra, Aaditya Singh et al.
Semi-supervised Vision Transformers at Scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro et al.
Optimizing Relevance Maps of Vision Transformers Improves Robustness
Hila Chefer, Idan Schwartz, Lior Wolf
Vision Transformers provably learn spatial structure
Samy Jelassi, Michael Sander, Yuanzhi Li
Can Vision Transformers Learn without Natural Images?
Kodai Nakashima, Hirokatsu Kataoka, Asato Matsumoto et al.
Less Is More: Pay Less Attention in Vision Transformers
Zizheng Pan, Bohan Zhuang, Haoyu He et al.
Vision Transformers Are Robust Learners
Sayak Paul, Pin-Yu Chen
Scaled ReLU Matters for Training Vision Transformers
Pichao Wang, Xue Wang, Hao Luo et al.
Towards Transferable Adversarial Attacks on Vision Transformers
Zhipeng Wei, Jingjing Chen, Micah Goldblum et al.
Width & Depth Pruning for Vision Transformers
Fang Yu, Kun Huang, Meng Wang et al.
MIA-Former: Efficient and Robust Vision Transformers via Multi-Grained Input-Adaptation
Zhongzhi Yu, Yonggan Fu, Sicheng Li et al.