{"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/dense-temporal-convolution-network-for-sign","title":"Dense Temporal Convolution Network for Sign Language Translation","arxiv_id":null,"date":"2019-05-01","proceeding":"IJCAI 2019 5","authors":["Dan Guo; Shuo Wang; Qi Tian;Meng Wang"],"abstract":"The sign language translation (SLT) which aims at translating a sign language video into natural language is weakly supervised given that there is no exact mapping relationship between visual actions and textual words in a sentence label.\r\nTo align the sign language actions and translate them into the respective words automatically, this paper proposes a dense temporal convolution network, termed \\emph{DenseTCN} which captures the actions in hierarchical views. \r\nWithin this network, a temporal convolution (TC) is designed to learn the short-term correlation among adjacent features and further extended to a dense hierarchical structure. In the $k^\\mathrm{th}$ TC layer, we integrate the outputs of all preceding layers together: (1) The TC in a deeper layer essentially has larger receptive fields, which captures long-term temporal context by the hierarchical content transition. (2) The integration addresses the SLT problem by different views, including embedded short-term and extended long-term sequential learning. Finally, we adopt the CTC loss and a fusion strategy to learn the feature-wise classification and generate the translated sentence. The experimental results on two popular sign language benchmarks, \\emph{i.e.} PHOENIX and USTC-ConSents, demonstrate the effectiveness of our proposed method in terms of various measurements.","url_abs":"https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=qTE3BacAAAAJ&citation_for_view=qTE3BacAAAAJ:u-x6o8ySG0sC","url_pdf":"https://www.ijcai.org/proceedings/2019/0105.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":[],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sign-language-recognition","task_name":"Sign Language Recognition"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sign-language-recognition-on-rwth-phoenix","task":"Sign Language Recognition","dataset":"RWTH-PHOENIX-Weather 2014","model":"DTN","rank_in_archive_order":20,"of":22,"metrics":{"Word Error Rate (WER)":"36.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}