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C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing
To this end, we introduce C-SAW, a method that complements CLIP with a self-supervised loss in the visual space and a novel prompt learning technique that emphasizes both visual domain and content-specific features.
Learning Class and Domain Augmentations for Single-Source Open-Domain Generalization
Single-source open-domain generalization (SS-ODG) addresses the challenge of labeled source domains with supervision during training and unlabeled novel target domains during testing.
ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation
This is still a work in progress and so we abstain from including results in this paper.
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