{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:09:35Z","timestamp":1763017775706},"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 the problem of object co-segmentation in images. Object co-segmentation aims to segment common objects in images and has promising applications in AI agents. We solve it by proposing a co-occurrence map, which measures how likely an image region belongs to an object and also appears in other images. The co-occurrence map of an image is calculated by combining two parts: objectness scores of image regions and similarity evidences from object proposals across images. We introduce a deep-dense conditional random field framework to infer co-occurrence maps. Both similarity metric and objectness measure are learned end-to-end in a single deep network. We evaluate our method on two benchmarks and achieve competitive performance.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/471","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"3371-3377","source":"Crossref","is-referenced-by-count":40,"title":["Deep-dense Conditional Random Fields for Object Co-segmentation"],"prefix":"10.24963","author":[{"given":"Zehuan","family":"Yuan","sequence":"first","affiliation":[{"name":"Nanjing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Lu","sequence":"additional","affiliation":[{"name":"Nanjing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yirui","family":"Wu","sequence":"additional","affiliation":[{"name":"Hohai University, China"}],"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":"2017-07-28T07:54:04Z","timestamp":1501228444000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/471"}},"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\/471","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}