title: VisionReasoner: Unified Visual Perception and Reasoning via
    Reinforcement Learning
summary: 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).
paper_authors._id: 682be7b7a1a5d85b0537de81
paper_authors.name: Yuqi Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-20T07:21:05.792Z
paper_authors._id: 669cefd6119595d21b55a995
paper_authors.avatarUrl: /avatars/bafc2387ee70b263bf45c42159381da8.svg
paper_authors.fullname: Yuqi Liu
paper_authors.type: user
paper_authors.user: Ricky06662
paper_authors._id: 682be7b7a1a5d85b0537de82
paper_authors.name: Tianyuan Qu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:34:44.348Z
paper_authors._id: 66e79b3c1c79fc2e51dc1d60
paper_authors.avatarUrl: /avatars/8706336e9e7a417505c9bb32583a662f.svg
paper_authors.fullname: QU Tianyuan
paper_authors.type: user
paper_authors.user: TainU
paper_authors._id: 682be7b7a1a5d85b0537de83
paper_authors.name: Zhisheng Zhong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:35:08.921Z
paper_authors._id: 65d882d30f35ed3f52d3ae2c
paper_authors.avatarUrl: /avatars/22cda67c3fcd7150320ec3551eda90f5.svg
paper_authors.fullname: Zhisheng Zhong
paper_authors.type: user
paper_authors.user: zszhong
paper_authors._id: 682be7b7a1a5d85b0537de84
paper_authors.name: Bohao Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:35:16.720Z
paper_authors._id: 673a10f911b7efeeedabc252
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/T7ySn7F0pTVCvRdcvMz3d.png
paper_authors.fullname: Bohao Peng
paper_authors.type: user
paper_authors.user: BoHao0326
paper_authors._id: 682be7b7a1a5d85b0537de85
paper_authors.name: Shu Liu
paper_authors._id: 682be7b7a1a5d85b0537de86
paper_authors.name: Bei Yu
paper_authors._id: 682be7b7a1a5d85b0537de87
paper_authors.name: Jiaya Jia
paper_title: VisionReasoner: Unified Visual Perception and Reasoning via
    Reinforcement Learning
paper_summary: 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).
paper_ai_summary: VisionReasoner, a unified framework, excels in various visual perception tasks by employing innovative cognitive learning and reformulation strategies.
paper_ai_keywords: multi-object cognitive learning systematic task reformulation unified framework visual perception tasks structured reasoning process VisionReasoner Qwen2.5VL COCO (detection) ReasonSeg (segmentation) CountBench (counting)