activity
20172025
most citedUnsupervised Person Re-identification: Clustering and Fine-tuning

150 citations · 162 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2025

Salient Object Detection in Complex Weather Conditions via Noise Indicators

Quan Chen, Xiaokai Yang, Tingyu Wang +4

Salient object detection (SOD), a foundational task in computer vision, has advanced from single-modal to multi-modal paradigms to enhance generalization. However, most existing SO…

cs.CV2025★ 6 cited

Source-Free Object Detection with Detection Transformer

Huizai Yao, Sicheng Zhao, Shuo Lu +7

Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data. Most existi…

cs.CV2025★ 4 cited

TrackletGait: A Robust Framework for Gait Recognition in the Wild

Shaoxiong Zhang, Jinkai Zheng, Shangdong Zhu +1

Gait recognition aims to identify individuals based on their body shape and walking patterns. Though much progress has been achieved driven by deep learning, gait recognition in re…

cs.CV2024

It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment

Jinkai Zheng, Xinchen Liu, Boyue Zhang +4

Existing studies for gait recognition primarily utilized sequences of either binary silhouette or human parsing to encode the shapes and dynamics of persons during walking. Silhoue…

cs.CV2024★ 1 cited

ThermalGaussian: Thermal 3D Gaussian Splatting

Rongfeng Lu, Hangyu Chen, Zunjie Zhu +5

Thermography is especially valuable for the military and other users of surveillance cameras. Some recent methods based on Neural Radiance Fields (NeRF) are proposed to reconstruct…

cs.IR2024★ 1 cited

CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence

Chaochao Chen, Jiaming Zhang, Yizhao Zhang +5

With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models.…