133 citations · 393 across the 12 of their papers we have counts for
12 papers · 1 filter
On Data Scaling in Masked Image Modeling
Zhenda Xie, Zheng Zhang, Yue Cao +4
An important goal of self-supervised learning is to enable model pre-training to benefit from almost unlimited data. However, one method that has recently become popular, namely ma…
Revealing the Dark Secrets of Masked Image Modeling
Zhenda Xie, Zigang Geng, Jingcheng Hu +3
Masked image modeling (MIM) as pre-training is shown to be effective for numerous vision downstream tasks, but how and where MIM works remain unclear. In this paper, we compare MIM…
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
Yixuan Wei, Han Hu, Zhenda Xie +5
Masked image modeling (MIM) learns representations with remarkably good fine-tuning performances, overshadowing previous prevalent pre-training approaches such as image classificat…
iCAR: Bridging Image Classification and Image-text Alignment for Visual Recognition
Yixuan Wei, Yue Cao, Zheng Zhang +4
Image classification, which classifies images by pre-defined categories, has been the dominant approach to visual representation learning over the last decade. Visual learning thro…
SimMIM: A Simple Framework for Masked Image Modeling
Zhenda Xie, Zheng Zhang, Yue Cao +5
This paper presents SimMIM, a simple framework for masked image modeling. We simplify recently proposed related approaches without special designs such as block-wise masking and to…
Swin Transformer V2: Scaling Up Capacity and Resolution
Ze Liu, Han Hu, Yutong Lin +9
Large-scale NLP models have been shown to significantly improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities l…
Self-Supervised Learning with Swin Transformers
Zhenda Xie, Yutong Lin, Zhuliang Yao +4
We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers a…
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning
Yansong Tang, Zhenyu Jiang, Zhenda Xie +4
Previous cycle-consistency correspondence learning methods usually leverage image patches for training. In this paper, we present a fully convolutional method, which is simpler and…
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning
Zhenda Xie, Yutong Lin, Zheng Zhang +3
Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning…
Parametric Instance Classification for Unsupervised Visual Feature Learning
Yue Cao, Zhenda Xie, Bin Liu +3
This paper presents parametric instance classification (PIC) for unsupervised visual feature learning. Unlike the state-of-the-art approaches which do instance discrimination in a…
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation
Zhenda Xie, Zheng Zhang, Xizhou Zhu +2
In the feature maps of CNNs, there commonly exists considerable spatial redundancy that leads to much repetitive processing. Towards reducing this superfluous computation, we propo…
Local Relation Networks for Image Recognition
Han Hu, Zheng Zhang, Zhenda Xie +1
The convolution layer has been the dominant feature extractor in computer vision for years. However, the spatial aggregation in convolution is basically a pattern matching process…