  Accurate visual understanding is imperative for advancing autonomous systems
and intelligent robots. Despite the powerful capabilities of vision-language
models (VLMs) in processing complex visual scenes, precisely recognizing
obscured or ambiguously presented visual elements remains challenging. To
tackle such issues, this paper proposes InsightSee, a multi-agent framework to
enhance VLMs' interpretative capabilities in handling complex visual
understanding scenarios. The framework comprises a description agent, two
reasoning agents, and a decision agent, which are integrated to refine the
process of visual information interpretation. The design of these agents and
the mechanisms by which they can be enhanced in visual information processing
are presented. Experimental results demonstrate that the InsightSee framework
not only boosts performance on specific visual tasks but also retains the
original models' strength. The proposed framework outperforms state-of-the-art
algorithms in 6 out of 9 benchmark tests, with a substantial advancement in
multimodal understanding.
