36 citations · 136 across the 25 of their papers we have counts for
7 papers · 1 filter
Feature Erasing and Diffusion Network for Occluded Person Re-Identification
Zhikang Wang, Feng Zhu, Shixiang Tang +3
Occluded person re-identification (ReID) aims at matching occluded person images to holistic ones across different camera views. Target Pedestrians (TP) are usually disturbed by No…
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective
Yizhou Wang, Shixiang Tang, Feng Zhu +4
The pretrain-finetune paradigm is a classical pipeline in visual learning. Recent progress on unsupervised pretraining methods shows superior transfer performance to their supervis…
Achieving adjustable elasticity with non-affine to affine transition
Xiangying Shen, Chenchao Fang, Zhipeng Jin +7
For various engineering and industrial applications it is desirable to realize mechanical systems with broadly adjustable elasticity to respond flexibly to the external environment…
Layerwise Optimization by Gradient Decomposition for Continual Learning
Shixiang Tang, Dapeng Chen, Jinguo Zhu +2
Deep neural networks achieve state-of-the-art and sometimes super-human performance across various domains. However, when learning tasks sequentially, the networks easily forget th…
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification
Shijie Yu, Feng Zhu, Dapeng Chen +5
Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches still suffer from considerable performance degradation when un…
Complementary Relation Contrastive Distillation
Jinguo Zhu, Shixiang Tang, Dapeng Chen +5
Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation o…
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
Shixiang Tang, Peng Su, Dapeng Chen +1
Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algori…