1 code implementation • 23 Nov 2020 • Zheng Wang, Xin Yuan, Toshihiko Yamasaki, Yutian Lin, Xin Xu, Wenjun Zeng
In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i. e.}, all returned images are considered as the target.
2 code implementations • 21 Mar 2017 • Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu, Zhilan Hu, Chenggang Yan, Yi Yang
Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions.
Ranked #75 on Person Re-Identification on DukeMTMC-reID
2 code implementations • 16 Aug 2021 • Tianyang Liu, Yutian Lin, Bo Du
State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning.
1 code implementation • CVPR 2020 • Yutian Lin, Lingxi Xie, Yu Wu, Chenggang Yan, Qi Tian
Person re-identification (re-ID) is an important topic in computer vision.
no code implementations • CVPR 2018 • Yu Wu, Yutian Lin, Xuanyi Dong, Yan Yan, Wanli Ouyang, Yi Yang
We focus on the one-shot learning for video-based person re-Identification (re-ID).
no code implementations • 4 Nov 2021 • Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du
In this way, the model could access more variant data samples of an instance and keep predicting invariant discriminative representations for them.
no code implementations • 16 Feb 2023 • Zhihao Qian, Yutian Lin, Bo Du
In this paper, we propose a Patch-Mixed Cross-Modality framework (PMCM), where two images of the same person from two modalities are split into patches and stitched into a new one for model learning.
no code implementations • 21 Jan 2024 • Yunke Wang, Linwei Tao, Bo Du, Yutian Lin, Chang Xu
Adversarial Imitation Learning (AIL) allows the agent to reproduce expert behavior with low-dimensional states and actions.
no code implementations • 2 Apr 2024 • Tianhao Zhao, Yongcan Chen, Yu Wu, Tianyang Liu, Bo Du, Peilun Xiao, Shi Qiu, Hongda Yang, Guozhen Li, Yi Yang, Yutian Lin
In the first stage, we train a BEV autoencoder to reconstruct the BEV segmentation maps given corrupted noisy latent representation, which urges the decoder to learn fundamental knowledge of typical BEV patterns.