{"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/noisy-correspondence-learning-with-meta","title":"Noisy Correspondence Learning with Meta Similarity Correction","arxiv_id":"2304.06275","date":"2023-04-13","proceeding":"CVPR 2023 1","authors":["Haochen Han","Kaiyao Miao","Qinghua Zheng","Minnan Luo"],"abstract":"Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such noisy correspondence datasets causes performance degradation because the cross-modal retrieval methods can wrongly enforce the mismatched data to be similar. To tackle this problem, we propose a Meta Similarity Correction Network (MSCN) to provide reliable similarity scores. We view a binary classification task as the meta-process that encourages the MSCN to learn discrimination from positive and negative meta-data. To further alleviate the influence of noise, we design an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples. Extensive experiments are conducted to demonstrate the strengths of our method in both synthetic and real-world noises, including Flickr30K, MS-COCO, and Conceptual Captions.","url_abs":"https://arxiv.org/abs/2304.06275v1","url_pdf":"https://arxiv.org/pdf/2304.06275v1.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":"noisy-correspondence-learning-with-meta","repo_url":"https://github.com/hhc1997/mscn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"cross-modal-retrieval","task_name":"Cross-Modal Retrieval"},{"task_slug":"cross-modal-retrieval-with-noisy","task_name":"Cross-modal retrieval with noisy correspondence"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cross-modal-retrieval-with-noisy-1","task":"Cross-modal retrieval with noisy correspondence","dataset":"CC152K","model":"MSCN","rank_in_archive_order":11,"of":15,"metrics":{"Image-to-text R@1":"40.1","Image-to-text R@10":"76.6","Image-to-text R@5":"65.7","R-Sum":"366.7","Text-to-image R@1":"40.6","Text-to-image R@10":"76.3","Text-to-image R@5":"67.4"},"uses_additional_data":false},{"leaderboard":"/sota/cross-modal-retrieval-with-noisy-3","task":"Cross-modal retrieval with noisy correspondence","dataset":"COCO-Noisy","model":"MSCN","rank_in_archive_order":8,"of":17,"metrics":{"Image-to-text R@1":"78.1","Image-to-text R@10":"98.8","Image-to-text R@5":"97.2","R-Sum":"524.6","Text-to-image R@1":"64.3","Text-to-image R@10":"95.8","Text-to-image R@5":"90.4"},"uses_additional_data":false},{"leaderboard":"/sota/cross-modal-retrieval-with-noisy-2","task":"Cross-modal retrieval with noisy correspondence","dataset":"Flickr30K-Noisy","model":"MSCN","rank_in_archive_order":12,"of":16,"metrics":{"Image-to-text R@1":"77.4","Image-to-text R@10":"97.6","Image-to-text R@5":"94.9","R-Sum":"501.9","Text-to-image R@1":"59.6","Text-to-image R@10":"89.2","Text-to-image R@5":"83.2"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2304.06275","atlas_url":"https://app.syntology.ai/?focus=2304.06275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06275"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/hhc1997/mscn","reach":null}],"summary":{"ran":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"1c8f6ad68539d266","entry":"HiddenLayer","repo":"hhc1997/mscn","repo_kind":"official","path":"models.py","file_url":"https://github.com/hhc1997/mscn/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1c8f6ad68539d266"}},{"code_sha256_prefix":"71891965c331acad","entry":"Meta_Sim","repo":"hhc1997/mscn","repo_kind":"official","path":"models.py","file_url":"https://github.com/hhc1997/mscn/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"71891965c331acad"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}