activity
20172021
most citedEANet: Enhancing Alignment for Cross-Domain Person Re-identification

35 citations · 123 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV2021

Face-NMS: A Core-set Selection Approach for Efficient Face Recognition

Yunze Chen, Junjie Huang, Jiagang Zhu +4

Recently, face recognition in the wild has achieved remarkable success and one key engine is the increasing size of training data. For example, the largest face dataset, WebFace42M…

cs.CV202124 cited

Masked Face Recognition Challenge: The WebFace260M Track Report

Zheng Zhu, Guan Huang, Jiankang Deng +9

According to WHO statistics, there are more than 204,617,027 confirmed COVID-19 cases including 4,323,247 deaths worldwide till August 12, 2021. During the coronavirus epidemic, al…

cs.CV2021

Structure-Aware Face Clustering on a Large-Scale Graph with Nodes

Shuai Shen, Wanhua Li, Zheng Zhu +4

Face clustering is a promising method for annotating unlabeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performanc…

cs.CV202124 cited

WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition

Zheng Zhu, Guan Huang, Jiankang Deng +8

In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training da…

cs.CV2020

AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation

Junjie Huang, Zheng Zhu, Guan Huang +1

Both appearance cue and constraint cue are vital for human pose estimation. However, there is a tendency in most existing works to overfitting the former and overlook the latter. I…

cs.RO2019

High Performance Visual Object Tracking with Unified Convolutional Networks

Zheng Zhu, Wei Zou, Guan Huang +2

Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks. Nonetheless, the chosen CNN features are always pre-trained in…