activity
20182021
most citedSelf-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

29 citations · 37 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV20211 cited

MOS: A Low Latency and Lightweight Framework for Face Detection, Landmark Localization, and Head Pose Estimation

Yepeng Liu, Zaiwang Gu, Shenghua Gao +3

With the emergence of service robots and surveillance cameras, dynamic face recognition (DFR) in wild has received much attention in recent years. Face detection and head pose esti…

cs.CV20212 cited

SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification

Haocong Rao, Xiping Hu, Jun Cheng +1

Person re-identification via 3D skeletons is an emerging topic with great potential in security-critical applications. Existing methods typically learn body and motion features fro…

cs.CV20212 cited

Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification

Haocong Rao, Shihao Xu, Xiping Hu +2

Skeleton-based person re-identification (Re-ID) is an emerging open topic providing great value for safety-critical applications. Existing methods typically extract hand-crafted fe…

cs.CV202029 cited

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

Haocong Rao, Siqi Wang, Xiping Hu +4

Gait-based person re-identification (Re-ID) is valuable for safety-critical applications, and using only 3D skeleton data to extract discriminative gait features for person Re-ID i…

cs.CV2018

Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading

Kang Zhou, Zaiwang Gu, Wen Liu +4

Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high dema…

cs.CV2018

Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks

Fusheng Hao, Jun Cheng, Lei Wang +4

Discriminative features are critical for machine learning applications. Most existing deep learning approaches, however, rely on convolutional neural networks (CNNs) for learning f…