  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.
