12 citations · 25 across the 19 of their papers we have counts for
5 papers · 1 filter
Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
Haojie Zhang, Yongyi Su, Xun Xu +1
The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prom…
Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization
Yongyi Su, Xun Xu, Kui Jia
Test-Time Adaptation aims to adapt source domain model to testing data at inference stage with success demonstrated in adapting to unseen corruptions. However, these attempts may f…
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…
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…
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…