May 14, 2024, 4:47 a.m. | Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian

cs.CV updates on arXiv.org arxiv.org

arXiv:2405.07921v1 Announce Type: new
Abstract: Going beyond mere fine-tuning of vision-language models (VLMs), learnable prompt tuning has emerged as a promising, resource-efficient alternative. Despite their potential, effectively learning prompts faces the following challenges: (i) training in a low-shot scenario results in overfitting, limiting adaptability and yielding weaker performance on newer classes or datasets; (ii) prompt-tuning's efficacy heavily relies on the label space, with decreased performance in large class spaces, signaling potential gaps in bridging image and class concepts. In this …

abstract adaptability alternative arxiv beyond challenges cs.cv fine-tuning language language models low overfitting performance prompt prompts prompt tuning results semantics text training type vision vision-language vision-language models vlm vlms

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