{"url":"/sota/image-retrieval-on-deeppatent","task":{"name":"Image Retrieval","url":"/task/image-retrieval","note":null},"dataset":{"name":"DeepPatent","url":"/dataset/deeppatent"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Image Retrieval** is a fundamental and long-standing computer vision task that involves finding images similar to a given query from a large database. It is often considered a form of fine-grained, instance-level classification. The task is integral to image recognition alongside [classification](/task/image-classification) and [cross-modal retrieval](/task/cross-modal-retrieva). By leveraging visual similarity and other criteria, image retrieval enables users to efficiently discover relevant images, making it a crucial tool in applications such as search and recommendation.\r\n\r\n<span class=\"description-source\">[Extending CLIP for Category-to-image Retrieval in E-commerce](https://arxiv.org/abs/2112.11294)</span>\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [DELF](https://github.com/tensorflow/models/tree/master/research/delf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["mean average precision"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mean average precision":"higher"}},"counts":{"rows":4,"rows_with_code":4,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SwinV2","metrics":{"mean average precision":"0.856"},"uses_additional_data":false,"paper_date":"2023-09-01","paper":"/paper/patent-image-retrieval-using-transformer","paper_url":"https://www.sciencedirect.com/science/article/abs/pii/S0172219023000479","paper_title":"Patent image retrieval using transformer-based deep metric learning","code":"https://github.com/L4Clippers/Patent-Image-Retrieval-Transformer-DML","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"PatentCLIP","metrics":{"mean average precision":"0.657"},"uses_additional_data":false,"paper_date":"2024-12-10","paper":"/paper/impact-a-large-scale-integrated-multimodal","paper_url":"https://openreview.net/forum?id=l0Ydsl10ci#discussion","paper_title":"IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents","code":"https://github.com/AI4Patents/IMPACT","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"EffNet","metrics":{"mean average precision":"0.622"},"uses_additional_data":false,"paper_date":"2023-02-20","paper":"/paper/patent-image-retrieval-using-cross-entropy","paper_url":"https://iwfcv2023.github.io/assets/Poster/P1-6%20Patent%20Image%20Retrieval%20Using%20Cross-entropy-based%20Metric%20Learning_Kotaro%20Higuchi.pdf","paper_title":"Patent Image Retrieval Using Cross-entropy-based Metric Learning","code":"https://github.com/L4Clippers/Patent-Image-Retrieval-Transformer-DML","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"Res50","metrics":{"mean average precision":"0.379"},"uses_additional_data":false,"paper_date":"2022-01-01","paper":"/paper/deeppatent-large-scale-patent-drawing","paper_url":"https://ieeexplore.ieee.org/document/9707064","paper_title":"DeepPatent: Large scale patent drawing recognition and retrieval","code":"https://github.com/GoFigure-LANL/DeepPatent-dataset","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}