The era of large reasoning models (LRM) has begun, bringing new opportunities and challenges to the computer vision community. The strong semantic intelligence of LLM and the long-chain reasoning ability of LRM have opened new frontiers in visual understanding and interpretation.

This workshop aims to bridge the gap between computer vision and large language/reasoning models, focusing on complex tasks requiring advanced reasoning capabilities. We will explore how models can comprehend complex relationships through slow-thinking approaches like Neuro-Symbolic reasoning, Chain-of-Thought, and Multi-step Reasoning, pushing beyond traditional fixed tasks to understand object interactions within complex scenes.

The goal is to bring together perspectives from computer vision, multimodal learning, and large language models to address outstanding challenges in multimodal reasoning and slow thinking in the context of large reasoning models, fostering more flexible and robust understanding in AI systems.