{"url":"/sota/multi-label-image-recognition-with-partial","task":{"name":"Multi-label Image Recognition with Partial Labels","url":"/task/multi-label-image-recognition-with-partial","note":null},"dataset":{"name":"MS-COCO-2014","url":null},"category":null,"categories":["Adversarial","Audio","Computer Code","Computer Vision","Graphs","Medical","Methodology","Music","Playing Games"],"category_note":"the archive's category list for this table covers most areas; treated as no area assigned","description":"Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each multi-label image, aims to train MLR models with partial labels to reduce the annotation cost. Since existing MLR datasets have complete labels, current works propose to randomly drop a certain proportion of positive and negative labels to create partially annotated datasets, and report the results on the known labels proportion of 10% to 90%.","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":["Average mAP"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Average mAP":"higher"}},"counts":{"rows":6,"rows_with_code":6,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"DualCoOp+TaI-DPT","metrics":{"Average mAP":"83.6"},"uses_additional_data":false,"paper_date":"2022-11-23","paper":"/paper/texts-as-images-in-prompt-tuning-for-multi","paper_url":"https://arxiv.org/abs/2211.12739v2","paper_title":"Texts as Images in Prompt Tuning for Multi-Label Image Recognition","code":"https://github.com/guozix/tai-dpt","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"DualCoOp","metrics":{"Average mAP":"81.9"},"uses_additional_data":false,"paper_date":"2022-06-20","paper":"/paper/dualcoop-fast-adaptation-to-multi-label","paper_url":"https://arxiv.org/abs/2206.09541v1","paper_title":"DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations","code":"https://github.com/sunxm2357/dualcoop","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"DSRB","metrics":{"Average mAP":"78.4"},"uses_additional_data":false,"paper_date":"2022-05-26","paper":"/paper/semantic-aware-representation-blending-for-1","paper_url":"https://arxiv.org/abs/2205.13092v3","paper_title":"Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels","code":"https://github.com/hcplab-sysu/hcp-mlr-pl","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"SARB","metrics":{"Average mAP":"77.9"},"uses_additional_data":false,"paper_date":"2022-03-04","paper":"/paper/semantic-aware-representation-blending-for","paper_url":"https://arxiv.org/abs/2203.02172v1","paper_title":"Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels","code":"https://github.com/hcplab-sysu/hcp-mlr-pl","n_code_links":1,"syntology":{"n_ran":6,"n_unverified":1,"n_samples":7,"n_pointer_only_licence":7}},{"rank_in_archive_order":5,"model":"HST","metrics":{"Average mAP":"77.9"},"uses_additional_data":false,"paper_date":"2022-05-23","paper":"/paper/heterogeneous-semantic-transfer-for-multi","paper_url":"https://arxiv.org/abs/2205.11131v4","paper_title":"Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels","code":"https://github.com/hcplab-sysu/hcp-mlr-pl","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"SST","metrics":{"Average mAP":"76.7"},"uses_additional_data":false,"paper_date":"2021-12-21","paper":"/paper/structured-semantic-transfer-for-multi-label","paper_url":"https://arxiv.org/abs/2112.10941v3","paper_title":"Structured Semantic Transfer for Multi-Label Recognition with Partial Labels","code":"https://github.com/hcplab-sysu/hcp-mlr-pl","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"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":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":6,"n_unverified":1,"n_samples":7,"n_pointer_only_licence":7,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":6,"n_unverified":1,"n_samples":7,"n_pointer_only_licence":7,"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"}}}