VLM Survey is a curated collection of vision-language models designed for computer vision tasks.
The project addresses the challenge of discovering and understanding the landscape of vision-language models by aggregating resources, implementations, and references in one place. It serves researchers and practitioners who need to explore available models, understand their capabilities, and identify which approaches suit their specific vision tasks. The collection covers models relevant to tasks spanning multiple modalities where vision and language understanding intersect.
Developers should consider this resource when surveying the vision-language model landscape, comparing different approaches, or seeking implementations and references for models in this category. It works best as a reference tool for teams evaluating which vision-language models to adopt for their projects rather than as a framework or library to integrate directly into applications. The project is particularly useful for those working on multi-modal tasks that require understanding both visual and textual information.
The project maintains a straightforward collection-based approach with periodic updates to reflect new models and research in the vision-language space. Development activity shows consistent engagement with the codebase through regular additions and refinements to the curated content.
