Large vision-language models exhibit inherent capabilities to handle diverse visual perception tasks. In this paper, we introduce VisionReasoner, a unified framework capable of reasoning and solving multiple visual perception tasks within a shared model. Specifically, by designing novel multi-object cognitive learning strategies and systematic task reformulation, VisionReasoner enhances its reasoning capabilities to analyze visual inputs, and addresses diverse perception tasks in a unified framework. The model generates a structured reasoning process before delivering the desired outputs responding to user queries. To rigorously assess unified visual perception capabilities, we evaluate VisionReasoner on ten diverse tasks spanning three critical domains: detection, segmentation, and counting. Experimental results show that VisionReasoner achieves superior performance as a unified model, outperforming Qwen2.5VL by relative margins of 29.1% on COCO (detection), 22.1% on ReasonSeg (segmentation), and 15.3% on CountBench (counting).
View on arXiv@article{liu2025_2505.12081,
title={ VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning },
author={ Yuqi Liu and Tianyuan Qu and Zhisheng Zhong and Bohao Peng and Shu Liu and Bei Yu and Jiaya Jia },
journal={arXiv preprint arXiv:2505.12081},
year={ 2025 }
}