Composed Multi-modal Retrieval: A Survey of Approaches and Applications

• URL: http://arxiv.org/abs/2503.01334v1
• Date: Mon, 03 Mar 2025 09:18:43 GMT
• Title: Composed Multi-modal Retrieval: A Survey of Approaches and Applications
• Authors: Kun Zhang, Jingyu Li, Zhe Li, Jingjing Zhang,
• Abstract summary: Composed Multi-modal Retrieval (CMR) enables users to retrieve images or videos by integrating a reference visual input with textual modifications.<n>CMR is poised to become a pivotal technology in next-generation retrieval systems.
• Score: 17.316062338546544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid growth of multi-modal data from social media, short video platforms, and e-commerce, content-based retrieval has become essential for efficiently searching and utilizing heterogeneous information. Over time, retrieval techniques have evolved from Unimodal Retrieval (UR) to Cross-modal Retrieval (CR) and, more recently, to Composed Multi-modal Retrieval (CMR). CMR enables users to retrieve images or videos by integrating a reference visual input with textual modifications, enhancing search flexibility and precision. This paper provides a comprehensive review of CMR, covering its fundamental challenges, technical advancements, and categorization into supervised, zero-shot, and semi-supervised learning paradigms. We discuss key research directions, including data augmentation, model architecture, and loss optimization in supervised CMR, as well as transformation frameworks and external knowledge integration in zero-shot CMR. Additionally, we highlight the application potential of CMR in composed image retrieval, video retrieval, and person retrieval, which have significant implications for e-commerce, online search, and public security. Given its ability to refine and personalize search experiences, CMR is poised to become a pivotal technology in next-generation retrieval systems. A curated list of related works and resources is available at: https://github.com/kkzhang95/Awesome-Composed-Multi-modal-Retrieval

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