4 papers
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
Chuang Zhu, Kebin Liu, Wenqi Tang +3
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to…
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation
Xinyang Huang, Chuang Zhu, Ruiying Ren +2
Semi-supervised domain adaptation (SSDA) has been extensively researched due to its ability to improve classification performance and generalization ability of models by using a sm…
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation
Xinyang Huang, Chuang Zhu, Bowen Zhang +1
Semi-supervised domain adaptation (SSDA) has been widely studied due to its ability to utilize a few labeled target data to improve the generalization ability of the model. However…
Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation
Xinyang Huang, Chuang Zhu, Kebin Liu +2
Existing few-shot segmentation (FSS) only considers learning support-query correlation and segmenting unseen categories under the precise pixel masks. However, the cost of a large…