[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84071-en":3,"doc-seo-84071-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84071,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","RFHNet Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval","Fine-grained food image retrieval is essential in computational gastronomy, enabling food traceability, dietary monitoring, and smart catering. Although hashing methods offer storage efficiency and fast Hamming-distance search, they often underperform when subtle local semantics and frequency-sensitive cues determine similarity. RFHNet introduces a cascaded hierarchical hashing network with Fine-grained Relation Modeling, Multi-Frequency Modulated Fusion, and Hierarchical Semantic Synergy to integrate global and local signals into discriminative codes. Experiments on six benchmarks show mAP gains of 4.44%–17.20% at 12 bits, outperforming state-of-the-art.","RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval  \nJunsong Wang  \nCollege of Computer Science and Artificial Intelligence, Ludong University Yantai, China [wangjunsong@m.ldu.edu.cn](wangjunsong@m.ldu.edu.cn)  \nWeiqing Min  \nInstitute of Computing Technology, Chinese Academy of Sciences Beijing, China [minweiqing@ict.ac.cn](minweiqing@ict.ac.cn)  \nGuorui Sheng  \nCollege of Computer Science and Artificial Intelligence, Ludong University Yantai, China [shengguorui@ldu.edu.cn](shengguorui@ldu.edu.cn)  \nTao Yao  \nCollege of Computer Science and Artificial Intelligence, Ludong University Yantai, China [yaotao@ldu.edu.cn](yaotao@ldu.edu.cn)  \nLili Wang∗ College of Computer Science and Artificial Intelligence, Ludong University Yantai, China [wanglili@ldu.edu.cn](wanglili@ldu.edu.cn)  \nShuqiang Jiang  \nUniversity of Chinese Academy of Sciences Beijing, China [sqjiang@ict.ac.cn](sqjiang@ict.ac.cn)  \narXiv :2607 .06 148v 1 [ cs .CV] 7 Jul 2026  \nAbstract  \nFine-grained food image retrieval is a key task in computational gastronomy, with applications in food traceability, dietary monitoring, and smart catering systems. Although hashing-based retrieval is attractive for large-scale search due to its storage efficiency and fast Hamming-distance computation, existing methods often perform poorly in fine-grained food scenarios, where subtle local semantics and frequency-sensitive visual cues are essential. To address this challenge, we propose RFHNet, a cascaded hierarchical hashing network that captures both global structure and fine-grained local details through multi-level representations. RFHNet includes three components: (1) Fine-grained Relation Modeling (FRM) to capture subtle visual differences among similar food components; (2) Multi-Frequency Modulated Fusion (MFMF) to extract informative multi-frequency features; and (3) Hierarchical Semantic Synergy (HSS) to adaptively integrate multi-level representations and generate discriminative hash codes. Experiments on six food-specific benchmarks show that RFHNet consistently outperforms state-ofthe-art hashing methods, with mAP gains of 4.44% to 17.20% at 12 bits. These results validate the effectiveness of RFHNet for largescale visual food retrieval and smart catering applications. The source code will be released upon publication.  \nCCS Concepts  \n• Information systems → Image search; • Computing methodologies → Visual content-based indexing and retrieval.  \nKeywords  \nFine-grained image retrieval, hash learning, multi-frequency information, food computing.  \n∗ Corresponding author  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. ICMR’26, Amsterdam, Netherlands  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2617-0/2026/06  \n[https://doi.org/10.1145/3805622.3810838](https://doi.org/10.1145/3805622.3810838)  \nACM Reference Format:  \nJunsong Wang, Weiqing Min, Guorui Sheng, Tao Yao, Lili Wang, and Shuqiang Jiang. 2026. RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval. In International Conference on Multimedia Retrieval (ICMR’26), June 16–19, 2026, Amsterdam, Netherlands. ACM, New York, NY, USA, 10 pages. [https://doi.org/10.1145/](https://doi.org/10.1145/)[ ](https://doi.org/10.1145/)3805622.3810838  \n1 Introduction  \nFood image retrieval is a fundamental task in food computing, supporting applications such as intelligent menu recommendation, dietary monitoring, and nutrition management [16, 32] . Compared with generic image retrieval, food-related scenarios pose unique challenges due to fine-grained visual variations caused by ingredient composition, cooking styles, and presentation conditions. These challenges are further amplified in large-scale settings, where efficient and discriminative retrieval becomes critical.  \nWith the increasing scale of food image datasets, Fine-Grained Image Retrieval (FGIR) [48] has","cbCaithpTXVjGJ13","https://ap.wps.com/l/cbCaithpTXVjGJ13","pdf",11941354,3,1,10,"English","en",105,"# Introduction\n## Problem background and challenges\n## Fine-grained image retrieval and hashing approaches\n## Limitations of existing methods\n# Method\n## Fine-grained Relation Modeling (FRM)\n## Multi-Frequency Modulated Fusion (MFMF)\n## Hierarchical Semantic Synergy (HSS)\n# Experiments\n## Benchmark results and comparisons\n# Conclusion","[{\"question\":\"What problem does RFHNet target?\",\"answer\":\"RFHNet targets large-scale fine-grained food image retrieval, where visually similar dishes and subtle local variations make discrimination difficult.\"},{\"question\":\"Why do existing hashing methods struggle in fine-grained food scenarios?\",\"answer\":\"Existing methods often fail to capture subtle spatial relations and are sensitive to both high-frequency noise and low-frequency artifacts, leading to unstable representations.\"},{\"question\":\"What are the main components of RFHNet?\",\"answer\":\"RFHNet consists of Fine-grained Relation Modeling (FRM), Multi-Frequency Modulated Fusion (MFMF), and Hierarchical Semantic Synergy (HSS) to model relations, extract multi-frequency features, and integrate multi-level representations for discriminative hash 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problem does RFHNet target?","Question",{"text":75,"@type":76},"RFHNet targets large-scale fine-grained food image retrieval, where visually similar dishes and subtle local variations make discrimination difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do existing hashing methods struggle in fine-grained food scenarios?",{"text":80,"@type":76},"Existing methods often fail to capture subtle spatial relations and are sensitive to both high-frequency noise and low-frequency artifacts, leading to unstable representations.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main components of RFHNet?",{"text":84,"@type":76},"RFHNet consists of Fine-grained Relation Modeling (FRM), Multi-Frequency Modulated Fusion (MFMF), and Hierarchical Semantic Synergy (HSS) to model relations, extract multi-frequency features, and integrate multi-level representations for discriminative hash 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