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How well does CLIP understand texture?
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We investigate how well CLIP understands texture in natural images described by natural language. To this end, we analyze CLIP's ability to: (1) perform zero-shot learning on various texture and material classification datasets; (2) represent compositional properties of texture such as red dots or yellow stripes on the Describable Texture in Detail(DTDD) dataset; and (3) aid fine-grained categorization of birds in photographs described by color and texture of their body parts.
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ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations
ADAM combines LLM-generated contextual labels, CLIP embeddings, and nearest-neighbor voting to label novel objects without a predefined class list.
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