English(EN) Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs

GRACE框架支持高效、量化的视觉语言模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了GRACE，一个结合知识蒸馏和感知量化训练的新框架，以提高视觉语言模型（VLMs）的效率。该方法旨在减少训练后量化通常会带来的准确性损失。GRACE使用置信度门控蒸馏和关系对齐来保留关键信息，同时限制模型容量，从而得到INT4模型，其性能优于FP16基线模型，并显著提高了速度和内存效率。 AI

影响该框架为显著降低VLMs的计算成本和内存占用提供了一条途径，有可能在资源受限的设备上实现更广泛的部署。

排序理由该集群包含一篇关于高效视觉语言模型新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Yanlong Chen, Amirhossein Habibian, Luca Benini, Yawei Li · 2026-05-05 04:00

基于置信度蒸馏的门控关系对齐，用于高效视觉语言模型

arXiv:2601.22709v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post-training quantization often causes significant accuracy loss. Despite its potential, quantization-aware training for VLMs rem…

本文如何被排名

报道来源 [1]

基于置信度蒸馏的门控关系对齐，用于高效视觉语言模型

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