{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T03:13:01Z","timestamp":1781838781523,"version":"3.54.5"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"01","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>We propose a temporal action detection by spatial segmentation framework, which simultaneously categorize actions and temporally localize action instances in untrimmed videos. The core idea is the conversion of temporal detection task into a spatial semantic segmentation task. Firstly, the video imprint representation is employed to capture the spatial\/temporal interdependences within\/among frames and represent them as spatial proximity in a feature space. Subsequently, the obtained imprint representation is spatially segmented by a fully convolutional network. With such segmentation labels projected back to the video space, both temporal action boundary localization and per-frame spatial annotation can be obtained simultaneously. The proposed framework is robust to variable lengths of untrimmed videos, due to the underlying fixed-size imprint representations. The efficacy of the framework is validated in two public action detection datasets.<\/jats:p>","DOI":"10.1609\/aaai.v33i01.33018328","type":"journal-article","created":{"date-parts":[[2019,8,21]],"date-time":"2019-08-21T07:44:52Z","timestamp":1566373492000},"page":"8328-8335","source":"Crossref","is-referenced-by-count":19,"title":["Video Imprint Segmentation for Temporal Action Detection in Untrimmed Videos"],"prefix":"10.1609","volume":"33","author":[{"given":"Zhanning","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qilin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenxing","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanning","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2019,7,17]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4846\/4719","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4846\/4719","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T06:30:43Z","timestamp":1667802643000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/4846"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2019,7,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v33i01.33018328","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}