English(EN) Enhancing Vision Foundation Models via Multimodal Continual Pre-Training

新框架通过多模态持续预训练增强视觉模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-21 04:00

研究人员开发了一个多模态持续预训练（M-CPT）框架，以增强现有的视觉基础模型（VFMs）。该框架允许VFMs处理不同分辨率的视觉输入，并更好地将视觉表示与文本表示对齐。实验表明，M-CPT在不牺牲分类和分割等标准视觉任务性能的情况下，提高了多模态理解能力，即使将其应用于DINOv2、SigLIP和AIMv2等模型也是如此。 AI

影响增强现有视觉模型，以实现更好的多模态理解和灵活的分辨率处理。

排序理由研究论文，详细介绍了用于增强视觉基础模型的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Newsworthiness bucket: Tool; 研究论文，详细介绍了用于增强视觉基础模型的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CV TIER_1 English(EN) · Yitong Chen, Lingchen Meng, Wujian Peng, Jun Tao, Chenjie Xu, Zuxuan Wu, Yu-Gang Jiang · 2026-07-21 04:00

通过多模态持续预训练增强视觉基础模型

arXiv:2503.18931v3 Announce Type: replace Abstract: Vision Foundation Models (VFMs) provide strong visual representations for a wide range of applications. In this work, we enhance prevailing VFMs through multimodal training, allowing them to effectively process visual inputs at …

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通过多模态持续预训练增强视觉基础模型

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