LOVM: Language-Only Vision Model Selection

• URL: http://arxiv.org/abs/2306.08893v1
• Date: Thu, 15 Jun 2023 06:53:05 GMT
• Title: LOVM: Language-Only Vision Model Selection
• Authors: Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang, Serena Yeung
• Abstract summary: We introduce a new task LOVM: Language-Only Vision Model Selection, where methods are expected to perform both model selection and performance prediction. We then introduced an extensive LOVM benchmark consisting of ground-truth evaluations of 35 pre-trained VLMs and 23 datasets.
• Score: 13.857583570058392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular due to their exceptional performance on downstream vision applications, particularly in the few- and zero-shot settings. However, selecting the best-performing VLM for some downstream applications is non-trivial, as it is dataset and task-dependent. Meanwhile, the exhaustive evaluation of all available VLMs on a novel application is not only time and computationally demanding but also necessitates the collection of a labeled dataset for evaluation. As the number of open-source VLM variants increases, there is a need for an efficient model selection strategy that does not require access to a curated evaluation dataset. This paper proposes a novel task and benchmark for efficiently evaluating VLMs' zero-shot performance on downstream applications without access to the downstream task dataset. Specifically, we introduce a new task LOVM: Language-Only Vision Model Selection, where methods are expected to perform both model selection and performance prediction based solely on a text description of the desired downstream application. We then introduced an extensive LOVM benchmark consisting of ground-truth evaluations of 35 pre-trained VLMs and 23 datasets, where methods are expected to rank the pre-trained VLMs and predict their zero-shot performance.

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