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DiCE-CIR introduces direct composition learning for efficient zero-shot image retrieval

Researchers have introduced DiCE-CIR, a novel direct composition learning method for efficient zero-shot composed image retrieval. This approach bypasses the complex projection and re-encoding steps of previous methods by directly composing reference images with edit texts. DiCE-CIR utilizes a large language model to automatically generate training samples from image-caption pairs, enabling scalable training without manual annotation. The method has demonstrated state-of-the-art performance on the CIRCO benchmark and competitive results on CIRR, all while maintaining high computational efficiency. AI

IMPACT This method could improve the efficiency and accuracy of image retrieval systems that rely on multimodal queries.

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

Read on Hugging Face Daily Papers →

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

DiCE-CIR introduces direct composition learning for efficient zero-shot image retrieval

COVERAGE [2]

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

    DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval

    Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text describing the desired modification. Recent ZS-CIR studies have relied on projection-based methods that map a reference image into …

  2. arXiv cs.CV TIER_1 English(EN) · Gwang-Ho Na, Ho-Joong Kim, Seong-Whan Lee ·

    DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval

    arXiv:2607.04665v1 Announce Type: new Abstract: Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text describing the desired modification. Recent ZS-CIR studies have relied on projectio…