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Found 4 papers, 1 papers with code
Regularizing Self-training for Unsupervised Domain Adaptation via Structural Constraints
Self-training based on pseudo-labels has emerged as a dominant approach for addressing conditional distribution shifts in unsupervised domain adaptation (UDA) for semantic segmentation problems.
Learning Expressive Prompting With Residuals for Vision Transformers
Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model.
Unsupervised Domain Adaptation Via Pseudo-labels And Objectness Constraints
Crucially, the objectness constraint is agnostic to the ground-truth semantic segmentation labels and, therefore, remains appropriate for unsupervised adaptation settings.
On the Importance of Distractors for Few-Shot Classification
An effective approach to few-shot classification involves a prior model trained on a large-sample base domain, which is then finetuned over the novel few-shot task to yield generalizable representations.
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