English(EN) Efficient Quantization-Aware Distillation with Cross-Modal Alignment for Edge Vision-Language Models

新框架提升边缘视觉-语言模型效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一个新框架，以提高视觉-语言模型（VLMs）在边缘设备的效率。该方法统一了蒸馏和量化感知训练，确保了一致的优化和更好的性能，尤其是在非RGB模态方面。该方法利用跨注意力适配器，通过从RGB数据转移知识来增强非RGB表示，从而减小了模态差距并提高了部署效率。 AI

影响提高了资源受限的边缘设备上视觉-语言模型的效率和性能。

排序理由详细介绍新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.CV TIER_1 English(EN) · Jinwoo Jeon, GyuYeop Do, Yubin Lim, Nam-Joon Kim, Hyun Gon Ryu, Hyuk-Jae Lee, Byung-Jun Lee · 2026-09-16 04:00

面向边缘视觉语言模型的跨模态对齐高效量化感知蒸馏

arXiv:2609.16689v1 Announce Type: new Abstract: Large-scale vision-language models (VLM) such as CLIP enable strong open-vocabulary reasoning, yet deploying these capabilities on resource-constrained edge devices remains challenging. EdgeVL addresses this problem by distilling CL…