Align and Surpass Human Camouflaged Perception: Visual Refocus Reinforcement Fine-Tuning

• URL: http://arxiv.org/abs/2505.19611v1
• Date: Mon, 26 May 2025 07:27:18 GMT
• Title: Align and Surpass Human Camouflaged Perception: Visual Refocus Reinforcement Fine-Tuning
• Authors: Ruolin Shen, Xiaozhong Ji, Kai WU, Jiangning Zhang, Yijun He, HaiHua Yang, Xiaobin Hu, Xiaoyu Sun,
• Abstract summary: Current multi-modal models exhibit a notable misalignment with the human visual system when identifying objects that are visually assimilated into the background.<n>We build a visual system that mimicks human visual camouflaged perception to progressively and iteratively refocus' visual concealed content.
• Score: 18.13538667261998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current multi-modal models exhibit a notable misalignment with the human visual system when identifying objects that are visually assimilated into the background. Our observations reveal that these multi-modal models cannot distinguish concealed objects, demonstrating an inability to emulate human cognitive processes which effectively utilize foreground-background similarity principles for visual analysis. To analyze this hidden human-model visual thinking discrepancy, we build a visual system that mimicks human visual camouflaged perception to progressively and iteratively `refocus' visual concealed content. The refocus is a progressive guidance mechanism enabling models to logically localize objects in visual images through stepwise reasoning. The localization process of concealed objects requires hierarchical attention shifting with dynamic adjustment and refinement of prior cognitive knowledge. In this paper, we propose a visual refocus reinforcement framework via the policy optimization algorithm to encourage multi-modal models to think and refocus more before answering, and achieve excellent reasoning abilities to align and even surpass human camouflaged perception systems. Our extensive experiments on camouflaged perception successfully demonstrate the emergence of refocus visual phenomena, characterized by multiple reasoning tokens and dynamic adjustment of the detection box. Besides, experimental results on both camouflaged object classification and detection tasks exhibit significantly superior performance compared to Supervised Fine-Tuning (SFT) baselines.

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