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20182022
most citedComplementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

87 citations · 254 across the 10 of their papers we have counts for

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11 papers · 1 filter

cs.CV20222 cited

Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning

Minghao Chen, Fangyun Wei, Chong Li +1

Prior works on action representation learning mainly focus on designing various architectures to extract the global representations for short video clips. In contrast, many practic…

cs.CV202124 cited

Rethinking and Improving Relative Position Encoding for Vision Transformer

Kan Wu, Houwen Peng, Minghao Chen +2

Relative position encoding (RPE) is important for transformer to capture sequence ordering of input tokens. General efficacy has been proven in natural language processing. However…

cs.CV202116 cited

AutoFormer: Searching Transformers for Visual Recognition

Minghao Chen, Houwen Peng, Jianlong Fu +1

Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is chal…

cs.CV20213 cited

Salient Object Ranking with Position-Preserved Attention

Hao Fang, Daoxin Zhang, Yi Zhang +5

Instance segmentation can detect where the objects are in an image, but hard to understand the relationship between them. We pay attention to a typical relationship, relative salie…

cs.CV2021

One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

Minghao Chen, Houwen Peng, Jianlong Fu +1

Despite remarkable progress achieved, most neural architecture search (NAS) methods focus on searching for one single accurate and robust architecture. To further build models with…

cs.CV2021

Suppress-and-Refine Framework for End-to-End 3D Object Detection

Zili Liu, Guodong Xu, Honghui Yang +5

3D object detector based on Hough voting achieves great success and derives many follow-up works. Despite constantly refreshing the detection accuracy, these works suffer from hand…