144 citations · 292 across the 4 of their papers we have counts for
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cs.CV2021★ 41 cited
Refiner: Refining Self-attention for Vision Transformers
Daquan Zhou, Yujun Shi, Bingyi Kang +6
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs. Yet, they generally require much more data for model pre-training. Most…
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.CV2019★ 105 cited
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
Xiaoguang Tu, Jian Zhao, Zihang Jiang +6
3D face reconstruction from a single 2D image is a challenging problem with broad applications. Recent methods typically aim to learn a CNN-based 3D face model that regresses coeff…