Search Results for author: Xiaobin Chang

Found 7 papers, 2 papers with code

Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification

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.

Person Re-Identification

SATS: Self-Attention Transfer for Continual Semantic Segmentation

no code implementations15 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.

Continual Semantic Segmentation Knowledge Distillation +2

Learning Discriminative Prototypes with Dynamic Time Warping

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.

Action Segmentation Dynamic Time Warping +2

Disjoint Label Space Transfer Learning with Common Factorised Space

no code implementations6 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.

Unsupervised Domain Adaptation

Multi-Level Factorisation Net for Person Re-Identification

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.

Person Re-Identification

Scalable and Effective Deep CCA via Soft Decorrelation

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.

MULTI-VIEW LEARNING

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