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arXiv · 2508.04424

Composed Object Retrieval: Object-level Retrieval via Composed Expressions

Tong Wang·Guanyu Yang·Nian Liu·Zongyan Han·Jinxing Zhou·Salman Khan·Fahad Shahbaz Khan
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Abstract

Retrieving fine-grained visual content based on user intent remains a challenge in multimodal systems. Although current Composed Image Retrieval (CIR) methods combine reference images with retrieval texts, they are constrained to image-level matching and cannot localize specific objects. To this end, we propose Composed Object Retrieval (COR), a new object-level retrieval task that retrieves target object(s) from candidate objects in a target image and grounds the retrieved result with pixel-level masks. Given a reference object, its mask, a target image, and a retrieval text describing the desired modification, COR requires models to perform composed visual-textual reasoning rather than relying on explicit category names. This setting introduces several challenges, including fine-grained compositional matching, negative-object filtering under visually similar distractors, and flexible single- or multi-object retrieval. We construct COR125K, the first large-scale COR benchmark, containing 125,541 retrieval triplets across 408 categories with base/novel splits for evaluating category-level generalization. We also present CORE, a unified end-to-end model that integrates reference region encoding, adaptive vision-text interaction, and region-level contrastive learning to align composed representations with target objects while suppressing background and distractors. Extensive experiments demonstrate that CORE significantly outperforms existing CIR-based pipelines and strong baselines in both base and novel categories, establishing a simple and effective foundation for fine-grained object-level multimodal retrieval. Code will be released publicly at https://github.com/wangtong627/COR.

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Tong Wang, Guanyu Yang, Nian Liu, Zongyan Han, Jinxing Zhou, Salman Khan, Fahad Shahbaz Khan. 2025-08-06. Composed Object Retrieval: Object-level Retrieval via Composed Expressions. https://arxiv.org/abs/2508.04424

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