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Found 2 papers, 1 papers with code
Data Alignment for Zero-Shot Concept Generation in Dermatology AI
Our goal is to use these models to generate caption text that aligns well with both the clinical lexicon and with the natural human language used in CLIP's pre-training data.
MIMIC: Masked Image Modeling with Image Correspondences
We train multiple models with different masked image modeling objectives to showcase the following findings: Representations trained on our automatically generated MIMIC-3M outperform those learned from expensive crowdsourced datasets (ImageNet-1K) and those learned from synthetic environments (MULTIVIEW-HABITAT) on two dense geometric tasks: depth estimation on NYUv2 (1. 7%), and surface normals estimation on Taskonomy (2. 05%).
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