{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T06:54:26Z","timestamp":1773212066983,"version":"3.50.1"},"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>Most of existing scene parsing methods suffer from the serious problems of both inconsistent parsing results and object boundary shift. To tackle these problems, we first propose an iterative Global-residual Refinement Network (GRN) through exploiting global contextual information to predict the parsing residuals and iteratively smoothen the inconsistent parsing labels. Furthermore, we propose a Local-boundary Refinement Network (LRN) to learn the position-adaptive propagation coefficients so that local contextual information from neighbors can be optimally captured for refining object boundaries. Finally, we cascade the proposed two refinement networks after a fully residual convolutional neural network within a uniform framework. Extensive experiments on ADE20K and Cityscapes datasets well demonstrate the effectiveness of the two refinement methods for refining scene parsing predictions.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/479","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"3427-3433","source":"Crossref","is-referenced-by-count":25,"title":["Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions"],"prefix":"10.24963","author":[{"given":"Rui","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Lin","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Institute, 360 company"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jintao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuicheng","family":"Yan","sequence":"additional","affiliation":[{"name":"Qihoo 360 Artificial Intelligence Institute"},{"name":"Department of Electrical and Computer Engineering, National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"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":"2017-07-28T07:54:06Z","timestamp":1501228446000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/479"}},"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\/479","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}