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Found 5 papers, 4 papers with code
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation
Extensive experiments demonstrate that ATASeg bridges the performance gap between TTA methods and their supervised counterparts with only extremely few annotations, even one click for labeling surpasses known SOTA TTA methods by 2. 6% average mIoU on ACDC benchmark.
Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios
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 for the deployment of models in real-world applications.
Robust Test-Time Adaptation in Dynamic Scenarios
Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams.
Active Learning for Domain Adaptation: An Energy-Based Approach
Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains.
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation
Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network.
