no code implementations • 8 Jun 2023 • Yunpeng Zhai, Peixi Peng, Mengxi Jia, Shiyong Li, Weiqiang Chen, Xuesong Gao, Yonghong Tian
Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.
Knowledge Distillation Unsupervised Person Re-Identification
no code implementations • ICCV 2023 • Yangru Huang, Peixi Peng, Yifan Zhao, Yunpeng Zhai, Haoran Xu, Yonghong Tian
Efficient motion and appearance modeling are critical for vision-based Reinforcement Learning (RL).
no code implementations • ICCV 2023 • Yunpeng Zhai, Peixi Peng, Yifan Zhao, Yangru Huang, Yonghong Tian
Vision-based reinforcement learning (RL) depends on discriminative representation encoders to abstract the observation states.
2 code implementations • ECCV 2020 • Yunpeng Zhai, Qixiang Ye, Shijian Lu, Mengxi Jia, Rongrong Ji, Yonghong Tian
Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains unexplored.
Domain Adaptive Person Re-Identification Ensemble Learning +1
no code implementations • 3 Jul 2020 • Mengxi Jia, Yunpeng Zhai, Shijian Lu, Siwei Ma, Jian Zhang
RGB-Infrared (IR) cross-modality person re-identification (re-ID), which aims to search an IR image in RGB gallery or vice versa, is a challenging task due to the large discrepancy between IR and RGB modalities.
Cross-Modality Person Re-identification Person Re-Identification
no code implementations • CVPR 2020 • Yunpeng Zhai, Shijian Lu, Qixiang Ye, Xuebo Shan, Jie Chen, Rongrong Ji, Yonghong Tian
Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown.
Ranked #8 on Unsupervised Domain Adaptation on Duke to Market