XR: Cross-Modal Agents for Composed Image Retrieval
Retrieval is being redefined by agentic AI, demanding multimodal reasoning beyond conventional similarity-based paradigms. Composed Image Retrieval (CIR) exemplifies this shift as each query combines a reference image with textual modifications, requiring compositional understanding across modalities. While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. To address these limitations, we introduce XR, a training-free multi-agent framework that reframes retrieval as a progressively coordinated reasoning process. It orchestrates three specialized types of agents: imagination agents synthesize target representations through cross-modal generation, similarity agents perform coarse filtering via hybrid matching, and question agents verify factual consistency through targeted reasoning for fine filtering. Through progressive multi-agent coordination, XR iteratively refines retrieval to meet both semantic and visual query constraints, achieving up to a 38% gain over strong training-free and training-based baselines on FashionIQ, CIRR, and CIRCO, while ablations show each agent is essential. Code is available: https://01yzzyu.github.io/xr.github.io/.
69719a02c1c7409747bf95eb
Zhongyu Yang
claimed_verified
2026-01-22T17:12:19.376Z
654f99f74c8874c64d4e5664
/avatars/e9da0d688f91ae49db91d0ebebb3782a.svg
Zhongyu Yang
yzzyu
user
yzzyu
69719a02c1c7409747bf95ec
Wei Pang
69719a02c1c7409747bf95ed
Yingfang Yuan
claimed_verified
2026-03-23T11:01:51.738Z
63f94298a8397941c5a861d9
/avatars/8a2b1b0fafe08051fd220a606e02fe3b.svg
James
Jamesyuan
user
Jamesyuan
XR: Cross-Modal Agents for Composed Image Retrieval
Retrieval is being redefined by agentic AI, demanding multimodal reasoning beyond conventional similarity-based paradigms. Composed Image Retrieval (CIR) exemplifies this shift as each query combines a reference image with textual modifications, requiring compositional understanding across modalities. While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. To address these limitations, we introduce XR, a training-free multi-agent framework that reframes retrieval as a progressively coordinated reasoning process. It orchestrates three specialized types of agents: imagination agents synthesize target representations through cross-modal generation, similarity agents perform coarse filtering via hybrid matching, and question agents verify factual consistency through targeted reasoning for fine filtering. Through progressive multi-agent coordination, XR iteratively refines retrieval to meet both semantic and visual query constraints, achieving up to a 38% gain over strong training-free and training-based baselines on FashionIQ, CIRR, and CIRCO, while ablations show each agent is essential. Code is available: https://01yzzyu.github.io/xr.github.io/.
A multi-agent framework for compositional image retrieval that uses specialized agents for generation, filtering, and verification to improve semantic and visual query matching.
compositional image retrieval multi-agent framework cross-modal generation hybrid matching targeted reasoning