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Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class
April 26, 2024, 4:45 a.m. | Mazda Moayeri, Michael Rabbat, Mark Ibrahim, Diane Bouchacourt
cs.CV updates on arXiv.org arxiv.org
Abstract: Vision-language models enable open-world classification of objects without the need for any retraining. While this zero-shot paradigm marks a significant advance, even today's best models exhibit skewed performance when objects are dissimilar from their typical depiction. Real world objects such as pears appear in a variety of forms -- from diced to whole, on a table or in a bowl -- yet standard VLM classifiers map all instances of a class to a \it{single vector …
abstract advance arxiv beyond class classification cs.ai cs.cv cs.hc diversity language language models marks objects open-world paradigm per performance retraining type vector vision vision-language vision-language models while world zero-shot
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