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Intell. Syst. Technol."],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>\n            Real-time segmentation and understanding of driving scenes are crucial in autonomous driving. Traditional pixel-wise approaches extract scene information by segmenting all pixels in a frame, and hence are inefficient and slow. Proposal-wise approaches only learn from the proposed object candidates, but still require multiple steps on the expensive proposal methods. Instead, this work presents a fast\n            <jats:italic>single-shot segmentation<\/jats:italic>\n            strategy for video scene understanding. The proposed net, called S3-Net, quickly locates and segments\n            <jats:italic>target sub-scenes<\/jats:italic>\n            , and meanwhile extracts\n            <jats:italic>\n              <jats:bold>attention-aware time-series sub-scene features<\/jats:bold>\n            <\/jats:italic>\n            (\n            <jats:italic>\n              <jats:bold>ats-features<\/jats:bold>\n            <\/jats:italic>\n            ) as inputs to an\n            <jats:italic>\n              <jats:bold>attention-aware spatio-temporal model (ASM)<\/jats:bold>\n            <\/jats:italic>\n            . Utilizing tensorization and quantization techniques, S3-Net is intended to be lightweight for edge computing. Experiments results on CityScapes, UCF11, HMDB51, and MOMENTS datasets demonstrate that the proposed S3-Net achieves an accuracy improvement of 8.1% versus the 3D-CNN based approach on UCF11, a storage reduction of 6.9\u00d7 and an inference speed of 22.8 FPS on CityScapes with a GTX1080Ti GPU.\n          <\/jats:p>","DOI":"10.1145\/3470660","type":"journal-article","created":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T14:48:38Z","timestamp":1632494918000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["S3-Net: A Fast Scene Understanding Network by Single-Shot Segmentation for Autonomous Driving"],"prefix":"10.1145","volume":"12","author":[{"given":"Yuan","family":"Cheng","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Southern University of Science and Technology, Shanghai, China"}]},{"given":"Yuchao","family":"Yang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7046-3455","authenticated-orcid":false,"given":"Hai-Bao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Ngai","family":"Wong","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Pokfulam, Hong Kong"}]},{"given":"Hao","family":"Yu","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2021,9,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"David Acuna Huan Ling Amlan Kar and Sanja Fidler. 2018. 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