Today's paper introduces Prism, a novel framework for decoupling and assessing the capabilities of Vision Language Models (VLMs). Prism separates the perception and reasoning processes involved in visual question answering, allowing for systematic evaluation of both proprietary and open-source VLMs. The framework provides valuable insights into VLM capabilities and demonstrates potential as an efficient solution for vision-language tasks.
Prism is an innovative framework designed to address the intertwined challenges of perception and reasoning in solving visual problems. By separating perception and reasoning into two distinct stages, Prism enables a systematic comparison and evaluation of proprietary and open-source Vision Language Models (VLMs) in terms of their perception and reasoning capabilities. Combining a streamlined VLM focused on perception with a powerful Large Language Model (LLM) designed for reasoning, Prism achieves outstanding results in general visual language tasks while significantly reducing training and operational costs.
Prism is an innovative framework designed to address the intertwined challenges of perception and reasoning in solving visual problems. By separating perception and reasoning into two distinct stages, Prism enables a systematic comparison and evaluation of proprietary and open-source Vision Language Models (VLMs) in terms of their perception and reasoning capabilities. Combining a streamlined VLM focused on perception with a powerful Large Language Model (LLM) designed for reasoning, Prism achieves outstanding results in general visual language tasks while significantly reducing training and operational costs.