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arXiv Computer Vision By Zihao Zhang, Dayan Wu, Xinze Liu, Hengjie Zhu, Yiliang Zhu, Ding Wang, Peng Fu, Zheng Lin, Weiping Wang

MulVec: Fine-Grained Role-Aware Matching for Training-Free Zero-Shot Composed Image Retrieval

Read the original on arXiv Computer Vision →

MulVec is a training‑free zero‑shot composed image retrieval method that matches a target image to a gallery using a reference image and a text edit. It introduces a role‑aware query system that separates the target description into four retrieval roles—Global, Desired, Preserve, and Forbidden—each mapped to specific probe vectors. By combining global and local visual representations, MulVec achieves significant performance gains on CIRCO, CIRR, and FashionIQ datasets, improving CIRCO mAP@5 by up to 23.0% over prior methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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