133 citations · 363 across the 13 of their papers we have counts for
5 papers · 1 filter
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
Yujun Shi, Kuangqi Zhou, Jian Liang +5
Class Incremental Learning (CIL) aims at learning a multi-class classifier in a phase-by-phase manner, in which only data of a subset of the classes are provided at each phase. Pre…
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…
LV-BERT: Exploiting Layer Variety for BERT
Weihao Yu, Zihang Jiang, Fei Chen +2
Modern pre-trained language models are mostly built upon backbones stacking self-attention and feed-forward layers in an interleaved order. In this paper, beyond this stereotyped l…
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…
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…