{"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/continuous-sign-language-recognition-with","title":"Continuous Sign Language Recognition with Correlation Network","arxiv_id":"2303.03202","date":"2023-03-06","proceeding":"CVPR 2023 1","authors":["Lianyu Hu","Liqing Gao","Zekang Liu","Wei Feng"],"abstract":"Human body trajectories are a salient cue to identify actions in the video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition (CSLR) usually process frames independently, thus failing to capture cross-frame trajectories to effectively identify a sign. To handle this limitation, we propose correlation network (CorrNet) to explicitly capture and leverage body trajectories across frames to identify signs. In specific, a correlation module is first proposed to dynamically compute correlation maps between the current frame and adjacent frames to identify trajectories of all spatial patches. An identification module is then presented to dynamically emphasize the body trajectories within these correlation maps. As a result, the generated features are able to gain an overview of local temporal movements to identify a sign. Thanks to its special attention on body trajectories, CorrNet achieves new state-of-the-art accuracy on four large-scale datasets, i.e., PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. A comprehensive comparison with previous spatial-temporal reasoning methods verifies the effectiveness of CorrNet. Visualizations demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.","url_abs":"https://arxiv.org/abs/2303.03202v3","url_pdf":"https://arxiv.org/pdf/2303.03202v3.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":"continuous-sign-language-recognition-with","repo_url":"https://github.com/hulianyuyy/corrnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"continuous-sign-language-recognition-with","repo_url":"https://github.com/hulianyuyy/adaptsign","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"continuous-sign-language-recognition-with","repo_url":"https://github.com/hulianyuyy/corrnet_plus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"sign-language-recognition","task_name":"Sign Language Recognition"}],"methods":[{"method_slug":"csl","method_name":"CSL"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sign-language-recognition-on-csl-daily","task":"Sign Language Recognition","dataset":"CSL-Daily","model":"CorrNet","rank_in_archive_order":10,"of":14,"metrics":{"Word Error Rate (WER)":"30.1"},"uses_additional_data":false},{"leaderboard":"/sota/sign-language-recognition-on-rwth-phoenix","task":"Sign Language Recognition","dataset":"RWTH-PHOENIX-Weather 2014","model":"CorrNet + VAC + SMKD","rank_in_archive_order":6,"of":22,"metrics":{"Word Error Rate (WER)":"19.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2303.03202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.03202"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. 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