Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.08800 (cs)
[Submitted on 9 Mar 2026]
Title:Granulon: Awakening Pixel-Level Visual Encoders with Adaptive Multi-Granularity Semantics for MLLM
Authors:Junyuan Mao, Qiankun Li, Linghao Meng, Zhicheng He, Xinliang Zhou, Kun Wang, Yang Liu, Yueming Jin
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Abstract:Recent advances in multimodal large language models largely rely on CLIP-based visual encoders, which emphasize global semantic alignment but struggle with fine-grained visual understanding. In contrast, DINOv3 provides strong pixel-level perception yet lacks coarse-grained semantic abstraction, leading to limited multi-granularity reasoning. To address this gap, we propose Granulon, a novel DINOv3-based MLLM with adaptive granularity augmentation. Granulon introduces a text-conditioned granularity Controller that dynamically adjusts the visual abstraction level according to the semantic scope of the textual input, and an Adaptive Token Aggregation module that performs granularity-guided pooling and relation-aware clustering to produce compact, semantically rich visual tokens. This design enables unified "pixel-to-fine-to-coarse" reasoning within a single forward pass. Extensive and interpretable experiments demonstrate that Granulon improves accuracy by ~30% and reduces hallucination by ~20%, outperforming all visual encoders under identical settings.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.08800 [cs.CV] |
| (or arXiv:2603.08800v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08800
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View a PDF of the paper titled Granulon: Awakening Pixel-Level Visual Encoders with Adaptive Multi-Granularity Semantics for MLLM, by Junyuan Mao and 7 other authors
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