activity
20172022
most citedHydraPlus-Net: Attentive Deep Features for Pedestrian Analysis

90 citations · 277 across the 19 of their papers we have counts for

collaborators

31 papers

cs.CV2022

Federated Unsupervised Domain Adaptation for Face Recognition

Weiming Zhuang, Xin Gan, Yonggang Wen +3

Given labeled data in a source domain, unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, whose data distributions a…

cs.CV2021

Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

Ziniu Wan, Zhengjia Li, Maoqing Tian +3

3D human shape and pose estimation is the essential task for human motion analysis, which is widely used in many 3D applications. However, existing methods cannot simultaneously ca…

cs.CV20215 cited

GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer

Shuaicheng Li, Qianggang Cao, Lingbo Liu +4

Group activity recognition is a crucial yet challenging problem, whose core lies in fully exploring spatial-temporal interactions among individuals and generating reasonable group…

cs.CV2021

CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing

Daxuan Ren, Jianmin Zheng, Jianfei Cai +8

Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem. This paper presents CSG-Stump Net, an unsupervised en…

cs.CV2021

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency

Zhipeng Luo, Zhongang Cai, Changqing Zhou +7

Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. However, drastic performance degradation rema…

cs.DC2021

Collaborative Unsupervised Visual Representation Learning from Decentralized Data

Weiming Zhuang, Xin Gan, Yonggang Wen +2

Unsupervised representation learning has achieved outstanding performances using centralized data available on the Internet. However, the increasing awareness of privacy protection…