activity
20162022
most citedLocal-to-Global Self-Attention in Vision Transformers

22 citations · 66 across the 12 of their papers we have counts for

collaborators

18 papers

cs.CV20222 cited

Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification

Yanan Wang, Xuezhi Liang, Shengcai Liao

Recently, large-scale synthetic datasets are shown to be very useful for generalizable person re-identification. However, synthesized persons in existing datasets are mostly cartoo…

cs.CV202216 cited

Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond

Irtiza Hasan, Shengcai Liao, Jinpeng Li +2

Pedestrian detection is the cornerstone of many vision based applications, starting from object tracking to video surveillance and more recently, autonomous driving. With the rapid…

cs.CV20211 cited

Efficient Person Search: An Anchor-Free Approach

Yichao Yan, Jinpeng Li, Jie Qin +2

Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. To achieve this goal, state-of-the-art models typically add a re-id bran…

cs.CV2021

Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction

Fang Zhao, Wenhao Wang, Shengcai Liao +1

While single-view 3D reconstruction has made significant progress benefiting from deep shape representations in recent years, garment reconstruction is still not solved well due to…

cs.CV202122 cited

Local-to-Global Self-Attention in Vision Transformers

Jinpeng Li, Yichao Yan, Shengcai Liao +2

Transformers have demonstrated great potential in computer vision tasks. To avoid dense computations of self-attentions in high-resolution visual data, some recent Transformer mode…

cs.CV20211 cited

AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection

Hongsong Wang, Shengcai Liao, Ling Shao

Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. Unfortunately, it has received much less attention than supervised o…