AutoConcept: Training-Free Concept-Guided Reranking for Metadata-Available Composed Image Retrieval

Abstract:Composed image retrieval (CIR) retrieves a target image from a reference image and a text modification. This paper studies metadata-available CIR reranking, where a fixed CIR model first returns a candidate pool and gallery metadata is then used for second-stage concept-guided scoring. We introduce AutoConcept, a training-free reranker that converts concept evidence into an interpretable memory. AutoConcept filters noisy concepts, activates query-relevant positive constraints with an auxiliary negative penalty, and combines base retrieval scores with metadata-based concept-candidate alignment through inference-time calibration. On FashionIQ, AutoConcept yields significant early-rank improvements over WeiMoCIR and consistent plug-in gains on LinCIR candidate pools. Metadata-aware controls show that structured concept memory adds signal beyond direct query-text and extracted-attribute matching, while a query-only variant further supports the effectiveness of concept-level reranking. A supplementary real-human concept-label study indicates that the same memory interface can consume participant-provided evidence. These results position AutoConcept as an interpretable concept-memory reranker for product-style CIR galleries with available metadata.

Accepted regular paper at PRICAI 2026. 16 pages, 4 figures

Information Retrieval (cs.IR); Computation and Language (cs.CL)

I.4.7; I.2.10; H.3.3

arXiv:2609.01456 [cs.IR]

(or arXiv:2609.01456v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2609.01456 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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