{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/seeds-semantic-separable-diffusion","title":"SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection","arxiv_id":"2310.04689","date":"2023-10-07","proceeding":null,"authors":["Pengfei Zhou","Weiqing Min","Yang Zhang","Jiajun Song","Ying Jin","Shuqiang Jiang"],"abstract":"Food detection is becoming a fundamental task in food computing that supports various multimedia applications, including food recommendation and dietary monitoring. To deal with real-world scenarios, food detection needs to localize and recognize novel food objects that are not seen during training, demanding Zero-Shot Detection (ZSD). However, the complexity of semantic attributes and intra-class feature diversity poses challenges for ZSD methods in distinguishing fine-grained food classes. To tackle this, we propose the Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food Detection (ZSFD). SeeDS consists of two modules: a Semantic Separable Synthesizing Module (S$^3$M) and a Region Feature Denoising Diffusion Model (RFDDM). The S$^3$M learns the disentangled semantic representation for complex food attributes from ingredients and cuisines, and synthesizes discriminative food features via enhanced semantic information. The RFDDM utilizes a novel diffusion model to generate diversified region features and enhances ZSFD via fine-grained synthesized features. Extensive experiments show the state-of-the-art ZSFD performance of our proposed method on two food datasets, ZSFooD and UECFOOD-256. Moreover, SeeDS also maintains effectiveness on general ZSD datasets, PASCAL VOC and MS COCO. The code and dataset can be found at https://github.com/LanceZPF/SeeDS.","url_abs":"https://arxiv.org/abs/2310.04689v1","url_pdf":"https://arxiv.org/pdf/2310.04689v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"seeds-semantic-separable-diffusion","repo_url":"https://github.com/lancezpf/seeds","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"food-recommendation","task_name":"Food recommendation"},{"task_slug":"generalized-zero-shot-object-detection","task_name":"Generalized Zero-Shot Object Detection"},{"task_slug":null,"task_name":"Generalized Zero-Shot Object Detection on MS-COCO"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"zero-shot-object-detection","task_name":"Zero-Shot Object Detection"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"synthesizer","method_name":"Synthesizer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/zero-shot-object-detection-on-ms-coco","task":"Zero-Shot Object Detection","dataset":"MS-COCO","model":"SeeDS","rank_in_archive_order":2,"of":9,"metrics":{"Recall":"64","mAP":"20.6"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-object-detection-on-pascal-voc-07","task":"Zero-Shot Object Detection","dataset":"PASCAL VOC'07","model":"SeeDS","rank_in_archive_order":1,"of":7,"metrics":{"mAP":"68.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}