May 8, 2024, 4:43 a.m. | Shihong Liu, Zhiqiu Lin, Samuel Yu, Ryan Lee, Tiffany Ling, Deepak Pathak, Deva Ramanan

cs.LG updates on

arXiv:2309.05950v4 Announce Type: replace-cross
Abstract: Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. As such, we aim to develop a black-box approach to optimize VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or even output logits. We propose employing chat-based LLMs to …

abstract aim arxiv box capabilities cs.lg data datasets fine-tuning however language language models proprietary scale tasks type vision vision-language vision-language models vlms web

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