80 citations · 214 across the 9 of their papers we have counts for
9 papers
Dynamic Residual Classifier for Class Incremental Learning
Xiuwei Chen, Xiaobin Chang
The rehearsal strategy is widely used to alleviate the catastrophic forgetting problem in class incremental learning (CIL) by preserving limited exemplars from previous tasks. With…
Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed Analysis
Xiuwei Chen, Xiaobin Chang
Class incremental learning (CIL) aims to recognize both the old and new classes along the increment tasks. Deep neural networks in CIL suffer from catastrophic forgetting and some…
Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification
Chao Wu, Wenhang Ge, Ancong Wu +1
To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role. However, such cross-view training…
SATS: Self-Attention Transfer for Continual Semantic Segmentation
Yiqiao Qiu, Yixing Shen, Zhuohao Sun +4
Continually learning to segment more and more types of image regions is a desired capability for many intelligent systems. However, such continual semantic segmentation suffers fro…
Learning Discriminative Prototypes with Dynamic Time Warping
Xiaobin Chang, Frederick Tung, Greg Mori
Dynamic Time Warping (DTW) is widely used for temporal data processing. However, existing methods can neither learn the discriminative prototypes of different classes nor exploit s…
Disjoint Label Space Transfer Learning with Common Factorised Space
Xiaobin Chang, Yongxin Yang, Tao Xiang +1
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. It i…
Multi-Level Factorisation Net for Person Re-Identification
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. Recently develop…
The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching
Qian Yu, Xiaobin Chang, Yi-Zhe Song +2
Many vision problems require matching images of object instances across different domains. These include fine-grained sketch-based image retrieval (FG-SBIR) and Person Re-identific…
Scalable and Effective Deep CCA via Soft Decorrelation
Xiaobin Chang, Tao Xiang, Timothy M. Hospedales
Recently the widely used multi-view learning model, Canonical Correlation Analysis (CCA) has been generalised to the non-linear setting via deep neural networks. Existing deep CCA…