DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models
The recent explosive interest in the reasoning capabilities of large language
models, such as DeepSeek-R1, has demonstrated remarkable success through
reinforcement learning-based fine-tuning frameworks, exemplified by methods
like Group Relative Policy Optimization (GRPO). However, such reasoning
abilities remain underexplored and notably absent in vision foundation models,
including representation models like the DINO series. In this work, we propose
DINO-R1, the first such attempt to incentivize visual in-context
reasoning capabilities of vision foundation models using reinforcement
learning. Specifically, DINO-R1 introduces Group Relative Query
Optimization (GRQO), a novel reinforcement-style training strategy explicitly
designed for query-based representation models, which computes query-level
rewards based on group-normalized alignment quality. We also apply
KL-regularization to stabilize the objectness distribution to reduce the
training instability. This joint optimization enables dense and expressive
supervision across queries while mitigating overfitting and distributional
drift. Building upon Grounding-DINO, we train a series of DINO-R1 family models
that integrate a visual prompt encoder and a visual-guided query selection
mechanism. Extensive experiments on COCO, LVIS, and ODinW demonstrate that
DINO-R1 significantly outperforms supervised fine-tuning baselines, achieving
strong generalization in both open-vocabulary and closed-set visual prompting
scenarios.
683dee0d6416d41bef4739d4
Chenbin Pan
683dee0d6416d41bef4739d5
Wenbin He
683dee0d6416d41bef4739d6
Zhengzhong Tu
claimed_verified
2025-06-03T08:47:37.512Z
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/avatars/14975b45568f9c399c92c3986b6ce83e.svg
Zhengzhong Tu
user
vztu
683dee0d6416d41bef4739d7
Liu Ren
DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models
The recent explosive interest in the reasoning capabilities of large language
models, such as DeepSeek-R1, has demonstrated remarkable success through
reinforcement learning-based fine-tuning frameworks, exemplified by methods
like Group Relative Policy Optimization (GRPO). However, such reasoning
abilities remain underexplored and notably absent in vision foundation models,
including representation models like the DINO series. In this work, we propose
DINO-R1, the first such attempt to incentivize visual in-context
reasoning capabilities of vision foundation models using reinforcement
learning. Specifically, DINO-R1 introduces Group Relative Query
Optimization (GRQO), a novel reinforcement-style training strategy explicitly
designed for query-based representation models, which computes query-level
rewards based on group-normalized alignment quality. We also apply
KL-regularization to stabilize the objectness distribution to reduce the
training instability. This joint optimization enables dense and expressive
supervision across queries while mitigating overfitting and distributional
drift. Building upon Grounding-DINO, we train a series of DINO-R1 family models
that integrate a visual prompt encoder and a visual-guided query selection
mechanism. Extensive experiments on COCO, LVIS, and ODinW demonstrate that
DINO-R1 significantly outperforms supervised fine-tuning baselines, achieving
strong generalization in both open-vocabulary and closed-set visual prompting
scenarios.
DINO-R1 incorporates reinforcement learning to enhance visual in-context reasoning capabilities in vision foundation models, achieving better performance than supervised fine-tuning across various visual prompting scenarios.
DeepSeek-R1 reinforcement learning-based fine-tuning Group Relative Policy Optimization (GRPO) visual in-context reasoning vision foundation models DINO series DINO-R1 Group Relative Query Optimization (GRQO) KL-regularization objectness distribution Grounding-DINO visual prompt encoder visual-guided query selection mechanism COCO LVIS ODinW open-vocabulary closed-set visual prompting