ViPER: Empowering the Self-Evolution of Visual Perception Abilities in Vision-Language Model

• URL: http://arxiv.org/abs/2510.24285v1
• Date: Tue, 28 Oct 2025 10:42:57 GMT
• Title: ViPER: Empowering the Self-Evolution of Visual Perception Abilities in Vision-Language Model
• Authors: Juntian Zhang, Song Jin, Chuanqi Cheng, Yuhan Liu, Yankai Lin, Xun Zhang, Yufei Zhang, Fei Jiang, Guojun Yin, Wei Lin, Rui Yan,
• Abstract summary: ViPER is a self-bootstrapping framework specifically designed to enable iterative evolution through self-critiquing and self-prediction.<n>Qwen-Viper consistently demonstrates superior performance across different vision-language scenarios while maintaining generalizability.
• Score: 61.29164681694533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The limited capacity for fine-grained visual perception presents a critical bottleneck for Vision-Language Models (VLMs) in real-world applications. Addressing this is challenging due to the scarcity of high-quality data and the limitations of existing methods: supervised fine-tuning (SFT) often compromises general capabilities, while reinforcement fine-tuning (RFT) prioritizes textual reasoning over visual perception. To bridge this gap, we propose a novel two-stage task that structures visual perception learning as a coarse-to-fine progressive process. Based on this task formulation, we develop ViPER, a self-bootstrapping framework specifically designed to enable iterative evolution through self-critiquing and self-prediction. By synergistically integrating image-level and instance-level reconstruction with a two-stage reinforcement learning strategy, ViPER establishes a closed-loop training paradigm, where internally synthesized data directly fuel the enhancement of perceptual ability. Applied to the Qwen2.5-VL family, ViPER produces the Qwen-Viper series. With an average gain of 1.7% on seven comprehensive benchmarks spanning various tasks and up to 6.0% on fine-grained perception, Qwen-Viper consistently demonstrates superior performance across different vision-language scenarios while maintaining generalizability. Beyond enabling self-improvement in perceptual capabilities, ViPER provides concrete evidence for the reciprocal relationship between generation and understanding, a breakthrough to developing more autonomous and capable VLMs.

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