2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CV2023
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
Yushu Li, Xun Xu, Yongyi Su +1
Generalizing deep learning models to unknown target domain distribution with low latency has motivated research into test-time training/adaptation (TTT/TTA). Existing approaches of…
cs.CV2023★ 2 cited
STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization
Yijin Chen, Xun Xu, Yongyi Su +1
Domain adaptation helps generalizing object detection models to target domain data with distribution shift. It is often achieved by adapting with access to the whole target domain…
cs.LG2023★ 2 cited
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training
Yongyi Su, Xun Xu, Tianrui Li +1
Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenar…