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PeFuse framework uses Diffusion Models and LLMs for training-free Composed Image Retrieval

By PulseAugur Editorial · [2 sources] ·

Researchers have developed PeFuse, a novel training-free framework for Composed Image Retrieval (CIR). This method utilizes pre-trained Diffusion Models and Multimodal Large Language Models to bridge visual and textual modalities through generative conversion. PeFuse reformulates CIR into single-modality retrieval problems, bypassing the need for task-specific training and achieving competitive performance on standard benchmarks. AI

IMPACT This framework offers a more flexible and efficient approach to image retrieval by leveraging existing large models without additional training.

RANK_REASON The cluster describes a new research paper detailing a novel framework for image retrieval.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

PeFuse framework uses Diffusion Models and LLMs for training-free Composed Image Retrieval

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COVERAGE [2]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Luis A. Leiva ·

    Training-Free Pseudo-Fusion for Composed Image Retrieval with Diffusion Models and Multimodal Large Language Models

    Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combining a reference image with an auxiliary modality, usually text-based. This approach supports fine-grained search where the target…

  2. arXiv cs.CV TIER_1 English(EN) · Fan Xu, Luis A. Leiva ·

    Training-Free Pseudo-Fusion for Composed Image Retrieval with Diffusion Models and Multimodal Large Language Models

    arXiv:2608.23102v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combining a reference image with an auxiliary modality, usually text-based. This approa…

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