English(EN) Multi-Modal Object Re-Identification with Dual Semantic Guidance and Global-Local Mutual Modulation

新的跨模态重识别框架增强了语义感知和特征对齐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-03 04:00

研究人员开发了一个新的跨模态目标重识别（ReID）框架，该框架解决了利用语义先验和建模全局-局部表示的挑战。所提出的方法包括一个文本语义注入器（TSI）用于将文本特征与视觉令牌集成，一个掩码全局-局部调制器（MGLM）用于进行部件感知的跨模态交互，以及一个分层MoE融合（HMF）用于自适应特征聚合。在三个基准上的实验表明了该方法的有效性。 AI

影响这项研究可以提高跨不同数据类型识别对象的系统的准确性和鲁棒性。

排序理由这是一篇详细介绍计算机视觉问题新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Weixiang Zhou, Xingguo Xu, Yuhao Wang, Cong Wang, Yang Yang, Zhixun Su, Jinshan Pan · 2026-08-03 04:00

具有双重语义引导和全局-局部互调的多模态对象重识别

arXiv:2607.29207v1 Announce Type: new Abstract: Multi-modal object Re-Identification (ReID) aims to retrieve target instances by leveraging complementary information across modalities. However, existing methods suffer from two challenges. First, they often fail to exploit well-al…

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具有双重语义引导和全局-局部互调的多模态对象重识别

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