Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging
This work addresses the challenge of understanding and integrating multimodal abilities in AI systems, offering insights for researchers in computer vision and natural language processing, though it is incremental as it builds on existing model merging techniques.
The paper tackled the problem of combining perception and reasoning in Vision-Language Models (VLMs) by merging models across modalities, enabling training-free transfer of reasoning abilities from Large Language Models (LLMs) to VLMs, with findings showing that perception is encoded in early layers and reasoning in middle-to-late layers, and after merging, all layers contribute to reasoning.
Vision-Language Models (VLMs) combine visual perception with the general capabilities, such as reasoning, of Large Language Models (LLMs). However, the mechanisms by which these two abilities can be combined and contribute remain poorly understood. In this work, we explore to compose perception and reasoning through model merging that connects parameters of different models. Unlike previous works that often focus on merging models of the same kind, we propose merging models across modalities, enabling the incorporation of the reasoning capabilities of LLMs into VLMs. Through extensive experiments, we demonstrate that model merging offers a successful pathway to transfer reasoning abilities from LLMs to VLMs in a training-free manner. Moreover, we utilize the merged models to understand the internal mechanism of perception and reasoning and how merging affects it. We find that perception capabilities are predominantly encoded in the early layers of the model, whereas reasoning is largely facilitated by the middle-to-late layers. After merging, we observe that all layers begin to contribute to reasoning, whereas the distribution of perception abilities across layers remains largely unchanged. These observations shed light on the potential of model merging as a tool for multimodal integration and interpretation.