VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs

• URL: http://arxiv.org/abs/2509.25916v1
• Date: Tue, 30 Sep 2025 08:10:56 GMT
• Title: VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs
• Authors: Peng Liu, Haozhan Shen, Chunxin Fang, Zhicheng Sun, Jiajia Liao, Tiancheng Zhao,
• Abstract summary: Vision-Language Models (VLMs) excel at high-level scene understanding but falter on fine-grained perception tasks requiring precise localization.<n>We introduce VLM-FO1, a novel framework that overcomes this limitation by reframing object-centric perception into a robust feature retrieval task.<n>Our method operates as a plug-and-play module that integrates with any pre-trained VLM.
• Score: 13.486495756813078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) excel at high-level scene understanding but falter on fine-grained perception tasks requiring precise localization. This failure stems from a fundamental mismatch, as generating exact numerical coordinates is a challenging task for language-centric architectures. In this paper, we introduce VLM-FO1, a novel framework that overcomes this limitation by reframing object-centric perception from a brittle coordinate generation problem into a robust feature retrieval task. Our method operates as a plug-and-play module that integrates with any pre-trained VLM. It leverages a Hybrid Fine-grained Region Encoder (HFRE), featuring a dual vision encoder, to generate powerful region tokens rich in both semantic and spatial detail. A token-based referencing system then enables the LLM to seamlessly reason about and ground language in these specific visual regions. Experiments show that VLM-FO1 achieves state-of-the-art performance across a diverse suite of benchmarks, demonstrating exceptional capabilities in object grounding, region generational understanding, and visual region reasoning. Crucially, our two-stage training strategy ensures that these perception gains are achieved without compromising the base model's general visual understanding capabilities. VLM-FO1 establishes an effective and flexible paradigm for building perception-aware VLMs, bridging the gap between high-level reasoning and fine-grained visual grounding.

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