{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:36:49Z","timestamp":1723016209714},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>We address semantic video object segmentation via a novel cross-granularity hierarchical graphical model to integrate tracklet and object proposal reasoning with superpixel labeling. Tracklet characterizes varying spatial-temporal relations of video object which, however, quite often suffers from sporadic local outliers. In order to acquire high-quality tracklets, we propose a transductive inference model which is capable of calibrating short-range noisy object tracklets with respect to long-range dependencies and high-level context cues. In the center of this work lies a new paradigm of semantic video object segmentation beyond modeling appearance and motion of objects locally, where the semantic label is inferred by jointly exploiting multi-scale contextual information and spatial-temporal relations of video object. We evaluate our method on two popular semantic video object segmentation benchmarks and demonstrate that it advances the state-of-the-art by achieving superior accuracy performance than other leading methods.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/634","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"4544-4550","source":"Crossref","is-referenced-by-count":8,"title":["Cross-Granularity Graph Inference for Semantic Video Object Segmentation"],"prefix":"10.24963","author":[{"given":"Huiling","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Signal Processing, Tampere University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tinghuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Nokia Technologies, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Signal Processing, Tampere University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joni-Kristian","family":"K\u00e4m\u00e4r\u00e4inen","sequence":"additional","affiliation":[{"name":"Department of Signal Processing, Tampere University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:54:53Z","timestamp":1501228493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/634"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/634","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}