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Appl."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n            Multi-modal human action segmentation is a critical and challenging task with a wide range of applications. Nowadays, the majority of approaches concentrate on the fusion of dense signals (i.e., RGB, optical flow, and depth maps). However, the potential contributions of sparse IoT sensor signals, which can be crucial for achieving accurate recognition, have not been fully explored. To make up for this, we introduce a\n            <jats:bold>S<\/jats:bold>\n            parse s\n            <jats:bold>i<\/jats:bold>\n            gnal-\n            <jats:bold>g<\/jats:bold>\n            uided Transformer (\n            <jats:bold>SigFormer<\/jats:bold>\n            ) to combine both dense and sparse signals. We employ mask attention to fuse localized features by constraining cross-attention within the regions where sparse signals are valid. However, since sparse signals are discrete, they lack sufficient information about the temporal action boundaries. Therefore, in SigFormer, we propose to emphasize the boundary information at two stages to alleviate this problem. In the first feature extraction stage, we introduce an intermediate bottleneck module to jointly learn both category and boundary features of each dense modality through the inner loss functions. After the fusion of dense modalities and sparse signals, we then devise a two-branch architecture that explicitly models the interrelationship between action category and temporal boundary. Experimental results demonstrate that SigFormer outperforms the state-of-the-art approaches on a multi-modal action segmentation dataset from real industrial environments, reaching an outstanding F1 score of 0.958. The codes and pre-trained models have been made available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/LIUQI-creat\/SigFormer\">https:\/\/github.com\/LIUQI-creat\/SigFormer<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3657296","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T12:26:23Z","timestamp":1712751983000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SigFormer: Sparse Signal-guided Transformer for Multi-modal Action Segmentation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6238-6804","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences Institute of Information Engineering, Beijing, China and \rUniversity of the Chinese Academy of Sciences School of Cyber Security, Beijing, China and \rKey Laboratory of Cyberspace Security Defense, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4931-8821","authenticated-orcid":false,"given":"Xinchen","family":"Liu","sequence":"additional","affiliation":[{"name":"JD Explore Academy, JD.com Inc, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8398-6369","authenticated-orcid":false,"given":"Kun","family":"Liu","sequence":"additional","affiliation":[{"name":"JD.com Inc, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0673-0058","authenticated-orcid":false,"given":"Xiaoyan","family":"Gu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences Institute of Information Engineering, Beijing, China and \rUniversity of the Chinese Academy of Sciences School of Cyber Security, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1633-7575","authenticated-orcid":false,"given":"Wu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2022.104567"},{"volume-title":"ECCV","key":"e_1_3_1_3_2","unstructured":"Nadine Behrmann, S. 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