VisionReasoner: Unified Visual Perception and Reasoning via
  Reinforcement Learning
Authors: name: Yuqi Liu, name: Tianyuan Qu, name: Zhisheng Zhong, name: Bohao Peng, name: Shu Liu, name: Bei Yu, name: Jiaya Jia

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).