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Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval
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Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval
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Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images given a compositional query, consisting of a reference image and a modifying text-without relying on annotated training data. Existing approaches often generate a synthetic target text using large language models (LLMs) to serve as an intermediate anchor between the compositional query and the target image. Models are then trained to align the compositional query with the generated text, and separately align images with their corresponding texts using contrastive learning. However, this reliance on intermediate text introduces error propagation, as inaccuracies in query-to-text and text-to-image mappings accumulate, ultimately degrading retrieval performance. To address these problems, we propose a novel framework by employing a Multimodal Reasoning Agent (MRA) for ZS-CIR. MRA eliminates the dependence on textual intermediaries by directly constructing triplets, <reference image, modification text, target image>, using only unlabeled image data. By training on these synthetic triplets, our model learns to capture the relationships between compositional queries and candidate images directly. Extensive experiments on three standard CIR benchmarks demonstrate the effectiveness of our approach. On the FashionIQ dataset, our method improves Average R@10 by at least 7.5\% over existing baselines; on CIRR, it boosts R@1 by 9.6\%; and on CIRCO, it increases mAP@5 by 9.5\%.
Forward citations
Cited by 6 Pith papers
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DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories
The paper reframes image retrieval as agentic exploration over personal visual histories and shows the best tested multimodal agent scores only 28.7 exact match on its new DISBench benchmark.
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Thinking Before Retrieving: Robust Zero-Shot Composed Image Retrieval via Strategic Planning and Self-Criticism
PEC-CIR reframes zero-shot composed image retrieval as a multi-stage Planner-Executor-Critic process to reduce generative errors and improve retrieval stability.
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DeliCIR: Memory-Guided Test-Time Deliberation via Multi-Agent Collaboration for Composed Image Retrieval
PDF introduces a hierarchical Perception-to-Deliberation Framework with multi-agents, intent routing, and tournament-style test-time scaling to achieve SOTA results on ZS-CIR benchmarks CIRR, CIRCO, and FashionIQ.
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DeliCIR: Memory-Guided Test-Time Deliberation via Multi-Agent Collaboration for Composed Image Retrieval
Proposes PDF, a hierarchical multi-agent Perception-to-Deliberation Framework that adds experience self-evolution and test-time scaling to composed image retrieval, claiming SOTA on CIRR, CIRCO, and FashionIQ.
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CoVR-R:Reason-Aware Composed Video Retrieval
Zero-shot LMM reasoning over edit after-effects (states, phases, camera, tempo) plus a new CoVR-R benchmark yields large recall gains on implicit-effect composed video retrieval without task-specific training.
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DeliCIR: Memory-Guided Test-Time Deliberation via Multi-Agent Collaboration for Composed Image Retrieval
A hierarchical multi-agent framework that fuses three CLIP-based retrieval views and then applies tournament-style test-time reasoning reports the best published scores on CIRR, CIRCO, and FashionIQ.
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