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