← Back to all news
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.

Related stories

Hugging Face Trending Papers
Jul 6

DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval

Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text describing the desired modification. Recent ZS-CIR studies have relied on projection-based methods that map a reference image into pseudo-word tokens in the text embedding space.

llmsragmultimodalbenchmarkssafety
More like this →
arXiv Computer Vision
6d ago

Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching

The paper introduces Image Bundle Composition (IBC), a new paradigm that moves beyond point-wise image matching to dynamically assemble cohesive image bundles from large, unstructured photo collections. It presents IBCBench, a benchmark with over 109,000 images and 667 verified queries, and proposes BundleWeaver, an agentic framework that uses a Large Language Model for relational role discovery and a Vision‑Language Model for bundle verification. Experiments show that BundleWeaver outperforms existing embedding and decompose‑and‑rerank methods, underscoring the importance of relational composition over atomic scoring.

By Rong Shan, Tianyi Xu, Congmin Zheng, Wenteng Chen, Jiachen Zhu, Junjie Wu, Teng Wang, Weiwen Liu, Changwang Zhang, Weinan Zhang, Jun Wang, Jianghao Lin
llmsragagentsmultimodalbenchmarks
More like this →
Hugging Face Trending Papers
Jul 2

FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval

Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image by editing a reference image with a natural-language instruction, without relying on domain-specific annotated triplets. Most existing ZS-CIR methods rely on textual inversion to translate the reference image into pseudo-text tokens and then compose them with the instruction via simple concatenation in the text space, which can be lossy and brittle for fine-grained semantics.

ragdiffusionmultimodalbenchmarks
More like this →