A new paper evaluates how well multimodal foundation models, including GPT-4o and Gemini 1.5 Pro, perform on standard computer vision tasks. Researchers developed a prompt-chaining method to translate vision tasks into text-based formats for API-accessible models. The study found that while these models are respectable generalists, they do not yet match specialized computer vision models, performing better on semantic than geometric tasks. GPT-4o showed the strongest performance among non-reasoning models, though models with native image generation capabilities exhibited failure modes like hallucinated objects. AI
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IMPACT Assesses current multimodal model capabilities on vision tasks, highlighting limitations compared to specialized models.
RANK_REASON This is a research paper evaluating existing multimodal models on computer vision tasks.