10 citations · 13 across the 7 of their papers we have counts for
7 papers
Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains
Zhuo He, Shuang Li, Wenze Song +4
Endowing deep models with the ability to generalize in dynamic scenarios is of vital significance for real-world deployment, given the continuous and complex changes in data distri…
Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation
Mingjia Li, Shuang Li, Tongrui Su +3
Capitalizing on the complementary advantages of generative and discriminative models has always been a compelling vision in machine learning, backed by a growing body of research.…
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation
Longhui Yuan, Shuang Li, Zhuo He +1
Test-time adaptation (TTA) adapts the pre-trained models during inference using unlabeled test data and has received a lot of research attention due to its potential practical valu…
Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios
Shuang Li, Longhui Yuan, Binhui Xie +1
Test-time adaptation (TTA) adapts the pre-trained models to test distributions during the inference phase exclusively employing unlabeled test data streams, which holds great value…
Robust Test-Time Adaptation in Dynamic Scenarios
Longhui Yuan, Binhui Xie, Shuang Li
Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams. Most of the previous TTA methods have achieved great s…
Active Learning for Domain Adaptation: An Energy-Based Approach
Binhui Xie, Longhui Yuan, Shuang Li +3
Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential…
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation
Binhui Xie, Longhui Yuan, Shuang Li +2
Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network. However, r…