activity
20192024
most citedAll Tokens Matter: Token Labeling for Training Better Vision Transformers

144 citations · 432 across the 16 of their papers we have counts for

collaborators
Showing 2021Show all

7 papers · 1 filter

cs.CV2021★ 4 cited

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

Guangyan Chen, Meiling Wang, Yufeng Yue +2

Recent Transformer-based methods have achieved advanced performance in point cloud registration by utilizing advantages of the Transformer in order-invariance and modeling dependen…

cs.CV2021★ 23 cited

VOLO: Vision Outlooker for Visual Recognition

Li Yuan, Qibin Hou, Zihang Jiang +2

Visual recognition has been dominated by convolutional neural networks (CNNs) for years. Though recently the prevailing vision transformers (ViTs) have shown great potential of sel…

cs.CV2021★ 25 cited

Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition

Qibin Hou, Zihang Jiang, Li Yuan +3

In this paper, we present Vision Permutator, a conceptually simple and data efficient MLP-like architecture for visual recognition. By realizing the importance of the positional in…

cs.LG2021★ 3 cited

Continual Learning via Bit-Level Information Preserving

Yujun Shi, Li Yuan, Yunpeng Chen +1

Continual learning tackles the setting of learning different tasks sequentially. Despite the lots of previous solutions, most of them still suffer significant forgetting or expensi…

cs.CV2021★ 144 cited

All Tokens Matter: Token Labeling for Training Better Vision Transformers

Zihang Jiang, Qibin Hou, Li Yuan +5

In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs). Different from the standard training objective of ViT…

cs.LG2021★ 9 cited

Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization

Zeke Xie, Li Yuan, Zhanxing Zhu +1

It is well-known that stochastic gradient noise (SGN) acts as implicit regularization for deep learning and is essentially important for both optimization and generalization of dee…

cs.CV2021★ 32 cited

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

Li Yuan, Yunpeng Chen, Tao Wang +6

Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT mo…