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Found 4 papers, 3 papers with code
Boosting Vision-Language Models with Transduction
Transduction is a powerful paradigm that leverages the structure of unlabeled data to boost predictive accuracy.
Low-Rank Few-Shot Adaptation of Vision-Language Models
Recent progress in the few-shot adaptation of Vision-Language Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task.
On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?
Additionally, our method does not rely on ad hoc rules (e. g., confidence threshold) used in some previous test-time augmentation techniques to filter the augmented views.
Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillance
In this paper, we define a probabilistic framework and present a formal proof of an algorithm for the unsupervised many-to-infinity domain adaptation of posteriors.
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