1 code implementation • CVPR 2022 • Chao Wu, Wenhang Ge, AnCong Wu, Xiaobin Chang
To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role.
1 code implementation • 15 Mar 2022 • Yiqiao Qiu, Yixing Shen, Zhuohao Sun, Yanchong Zheng, Xiaobin Chang, Weishi Zheng, Ruixuan Wang
Considering that pixels belonging to the same class in each image often share similar visual properties, a class-specific region pooling is applied to provide more efficient relationship information for knowledge transfer.
Ranked #1 on Overlapped 25-25 on ADE20K
1 code implementation • CVPR 2021 • Xiaobin Chang, Frederick Tung, Greg Mori
We propose Discriminative Prototype DTW (DP-DTW), a novel method to learn class-specific discriminative prototypes for temporal recognition tasks.
no code implementations • 6 Dec 2018 • Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales
In this paper, a unified approach is presented to transfer learning that addresses several source and target domain label-space and annotation assumptions with a single model.
Ranked #19 on Unsupervised Domain Adaptation on Market to Duke
no code implementations • CVPR 2018 • Xiaobin Chang, Timothy M. Hospedales, Tao Xiang
Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels.
no code implementations • 22 Nov 2017 • Qian Yu, Xiaobin Chang, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales
Many vision problems require matching images of object instances across different domains.
no code implementations • CVPR 2018 • Xiaobin Chang, Tao Xiang, Timothy M. Hospedales
Specifically, exact decorrelation is replaced by soft decorrelation via a mini-batch based Stochastic Decorrelation Loss (SDL) to be optimised jointly with the other training objectives.