English(EN) CLIP-RD: Relational Distillation for Efficient CLIP Knowledge Distillation

新的CLIP-RD框架提高了模型蒸馏效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了CLIP-RD，一个新颖的关系蒸馏框架，旨在创建更高效的CLIP模型版本。该新方法通过显式建模教师和学生嵌入之间的多向关系，解决了现有技术的局限性。CLIP-RD引入了垂直关系蒸馏（VRD）来对齐每个模态内的相似性分布，以及跨关系蒸馏（XRD）来强制双向跨模态对称性。该框架成功地使学生嵌入的几何结构更接近教师的嵌入，在最小的额外训练开销下，比CLIP-KD提高了1.8个百分点。 AI

影响这项研究提供了一种更有效的方法来蒸馏大型视觉语言模型，有可能在资源受限的设备上实现更广泛的部署。

排序理由这是一篇详细介绍模型蒸馏新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jeannie Chung, Hanna Jang, Ingyeong Yang, Uiwon Hwang, Jaehyeong Sim · 2026-09-11 04:00

CLIP-RD：用于高效CLIP知识蒸馏的关系蒸馏

arXiv:2603.25383v4 Announce Type: replace Abstract: Contrastive Language-Image Pre-training (CLIP) demonstrates strong zero-shot generalization, but due to substantial computational and memory costs, distillation into lightweight models is required. Existing relational objectives…

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CLIP-RD：用于高效CLIP知识蒸馏的关系蒸馏

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