Kim, B.[Boah],
Kim, J.[Jeongsol],
Ye, J.C.[Jong Chul],
Task-Agnostic Vision Transformer for Distributed Learning of Image
Processing,
IP(32), 2023, pp. 203-218.
IEEE DOI
2301
Task analysis, Transformers, Servers, Distance learning,
Computer aided instruction, Tail, Head, Distributed learning,
task-agnostic learning
BibRef
Park, S.[Sangjoon],
Ye, J.C.[Jong Chul],
Multi-Task Distributed Learning Using Vision Transformer With Random
Patch Permutation,
MedImg(42), No. 7, July 2023, pp. 2091-2105.
IEEE DOI
2307
Task analysis, Transformers, Head, Tail, Servers, Multitasking,
Distance learning, Federated learning, split learning,
privacy preservation
BibRef
Kim, B.J.[Bum Jun],
Choi, H.[Hyeyeon],
Jang, H.[Hyeonah],
Lee, D.G.[Dong Gu],
Jeong, W.[Wonseok],
Kim, S.W.[Sang Woo],
Improved robustness of vision transformers via prelayernorm in patch
embedding,
PR(141), 2023, pp. 109659.
Elsevier DOI
2306
Vision transformer, Patch embedding, Contrast enhancement,
Robustness, Layer normalization, Convolutional neural network, Deep learning
BibRef
Kang, J.Y.[Jun-Yong],
Heo, B.[Byeongho],
Choe, J.[Junsuk],
Improving ViT interpretability with patch-level mask prediction,
PRL(187), 2025, pp. 73-79.
Elsevier DOI
2501
Vision Transformer, Interpretability, Weak supervision, Object localization
BibRef
Guo, Y.[Yong],
Stutz, D.[David],
Schiele, B.[Bernt],
Improving Robustness of Vision Transformers by Reducing Sensitivity
to Patch Corruptions,
CVPR23(4108-4118)
IEEE DOI
2309
BibRef
Nalmpantis, A.[Angelos],
Panagiotopoulos, A.[Apostolos],
Gkountouras, J.[John],
Papakostas, K.[Konstantinos],
Aziz, W.[Wilker],
Vision DiffMask: Faithful Interpretation of Vision Transformers with
Differentiable Patch Masking,
XAI4CV23(3756-3763)
IEEE DOI
2309
BibRef
Beyer, L.[Lucas],
Izmailov, P.[Pavel],
Kolesnikov, A.[Alexander],
Caron, M.[Mathilde],
Kornblith, S.[Simon],
Zhai, X.H.[Xiao-Hua],
Minderer, M.[Matthias],
Tschannen, M.[Michael],
Alabdulmohsin, I.[Ibrahim],
Pavetic, F.[Filip],
FlexiViT: One Model for All Patch Sizes,
CVPR23(14496-14506)
IEEE DOI
2309
BibRef
Chang, S.N.[Shu-Ning],
Wang, P.[Pichao],
Lin, M.[Ming],
Wang, F.[Fan],
Zhang, D.J.H.[David Jun-Hao],
Jin, R.[Rong],
Shou, M.Z.[Mike Zheng],
Making Vision Transformers Efficient from A Token Sparsification View,
CVPR23(6195-6205)
IEEE DOI
2309
BibRef
Phan, L.[Lam],
Nguyen, H.T.H.[Hiep Thi Hong],
Warrier, H.[Harikrishna],
Gupta, Y.[Yogesh],
Patch Embedding as Local Features: Unifying Deep Local and Global
Features via Vision Transformer for Image Retrieval,
ACCV22(II:204-221).
Springer DOI
2307
BibRef
Liu, Y.[Yue],
Matsoukas, C.[Christos],
Strand, F.[Fredrik],
Azizpour, H.[Hossein],
Smith, K.[Kevin],
PatchDropout: Economizing Vision Transformers Using Patch Dropout,
WACV23(3942-3951)
IEEE DOI
2302
Training, Image resolution, Computational modeling,
Biological system modeling, Memory management, Transformers,
Biomedical/healthcare/medicine
BibRef
Gu, J.D.[Jin-Dong],
Tresp, V.[Volker],
Qin, Y.[Yao],
Are Vision Transformers Robust to Patch Perturbations?,
ECCV22(XII:404-421).
Springer DOI
2211
BibRef
Li, Z.K.[Zhi-Kai],
Ma, L.P.[Li-Ping],
Chen, M.J.[Meng-Juan],
Xiao, J.R.[Jun-Rui],
Gu, Q.Y.[Qing-Yi],
Patch Similarity Aware Data-Free Quantization for Vision Transformers,
ECCV22(XI:154-170).
Springer DOI
2211
BibRef
Yun, S.[Sukmin],
Lee, H.[Hankook],
Kim, J.[Jaehyung],
Shin, J.[Jinwoo],
Patch-level Representation Learning for Self-supervised Vision
Transformers,
CVPR22(8344-8353)
IEEE DOI
2210
Training, Representation learning, Visualization, Neural networks,
Object detection, Self-supervised learning, Transformers,
Self- semi- meta- unsupervised learning
BibRef
Salman, H.[Hadi],
Jain, S.[Saachi],
Wong, E.[Eric],
Madry, A.[Aleksander],
Certified Patch Robustness via Smoothed Vision Transformers,
CVPR22(15116-15126)
IEEE DOI
2210
Visualization, Smoothing methods, Costs, Computational modeling,
Transformers, Adversarial attack and defense
BibRef
Tang, Y.[Yehui],
Han, K.[Kai],
Wang, Y.H.[Yun-He],
Xu, C.[Chang],
Guo, J.Y.[Jian-Yuan],
Xu, C.[Chao],
Tao, D.C.[Da-Cheng],
Patch Slimming for Efficient Vision Transformers,
CVPR22(12155-12164)
IEEE DOI
2210
Visualization, Quantization (signal), Computational modeling,
Aggregates, Benchmark testing,
Representation learning
BibRef
Chen, Z.Y.[Zhao-Yu],
Li, B.[Bo],
Wu, S.[Shuang],
Xu, J.H.[Jiang-He],
Ding, S.H.[Shou-Hong],
Zhang, W.Q.[Wen-Qiang],
Shape Matters: Deformable Patch Attack,
ECCV22(IV:529-548).
Springer DOI
2211
BibRef
Chen, Z.Y.[Zhao-Yu],
Li, B.[Bo],
Xu, J.H.[Jiang-He],
Wu, S.[Shuang],
Ding, S.H.[Shou-Hong],
Zhang, W.Q.[Wen-Qiang],
Towards Practical Certifiable Patch Defense with Vision Transformer,
CVPR22(15127-15137)
IEEE DOI
2210
Smoothing methods, Toy manufacturing industry, Semantics,
Network architecture, Transformers, Robustness,
Adversarial attack and defense
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Attention in Vision Transformers .