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New frameworks enhance zero-shot composed image retrieval accuracy

By PulseAugur Editorial · [6 sources] ·

Two new research papers introduce novel frameworks for zero-shot composed image retrieval, a task that involves identifying a target image based on a reference image and a textual modification. The first paper, PEC-CIR, proposes a multi-stage reasoning pipeline with a Planner-Executor-Critic architecture to improve query construction by evaluating candidate queries before retrieval. The second paper, FoCo, revisits proxy task design by modeling composition as two coordinated stages: focusing on modification-relevant visual content and then completing the target semantics, achieving state-of-the-art performance and improved generalization. AI

IMPACT These new frameworks could improve the accuracy and generalization of image retrieval systems that rely on textual modifications.

RANK_REASON Two academic papers published on arXiv introducing new methods for a specific AI task.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New frameworks enhance zero-shot composed image retrieval accuracy

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COVERAGE [6]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    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 i…

  2. arXiv cs.AI TIER_1 English(EN) · Jingjing Zhang, Lei Zhang, Zheren Fu, Zhendong Mao ·

    Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval

    arXiv:2607.00374v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification. While supervised CIR relies on costly triplets, Zero-Shot CIR (ZS-CIR) alleviates this reliance through proxy tasks trained…

  3. arXiv cs.AI TIER_1 English(EN) · Gunho Jung, Jeong-Woo Park, Seon Bin Kim, Seong-Whan Lee ·

    Thinking Before Retrieving: Robust Zero-Shot Composed Image Retrieval via Strategic Planning and Self-Criticism

    arXiv:2606.31222v1 Announce Type: new Abstract: Composed image retrieval requires identifying a target image from a gallery by integrating a reference image with a textual modification instruction. In a training-free zero-shot setting, this task relies on constructing a retrieval…

  4. arXiv cs.CL TIER_1 English(EN) · Zhendong Mao ·

    Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval

    Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification. While supervised CIR relies on costly triplets, Zero-Shot CIR (ZS-CIR) alleviates this reliance through proxy tasks trained on image-text pairs. However, existing proxy task…

  5. arXiv cs.CV TIER_1 English(EN) · Zhenqi He, Ziqi Jiang, Yuanpei Liu, Yanghao Wang, Teng Wang, Long Chen ·

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

    arXiv:2607.02284v1 Announce Type: new Abstract: 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 o…

  6. arXiv cs.CV TIER_1 English(EN) · Long Chen ·

    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 i…

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