6 papers
Domain-Conditioned Transformer for Fully Test-time Adaptation
Yushun Tang, Shuoshuo Chen, Jiyuan Jia +2
Fully test-time adaptation aims to adapt a network model online based on sequential analysis of input samples during the inference stage. We observe that, when applying a transform…
Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation
Yushun Tang, Shuoshuo Chen, Zhihe Lu +2
Transformer-based methods have achieved remarkable success in various machine learning tasks. How to design efficient test-time adaptation methods for transformer models becomes an…
Learning Visual Conditioning Tokens to Correct Domain Shift for Fully Test-time Adaptation
Yushun Tang, Shuoshuo Chen, Zhehan Kan +3
Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradat…
Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation
Zhehan Kan, Shuoshuo Chen, Ce Zhang +2
A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization problem. The learned network does not have the c…
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
Yushun Tang, Ce Zhang, Heng Xu +5
Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradat…
Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation
Zhehan Kan, Shuoshuo Chen, Zeng Li +1
We observe that human poses exhibit strong group-wise structural correlation and spatial coupling between keypoints due to the biological constraints of different body parts. This…