{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:09:34Z","timestamp":1781647774397,"version":"3.54.5"},"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>The parsing of building facades is a key component to the problem of 3D street\n\n  scenes reconstruction, which is long desired in computer vision. In this\n\n  paper, we propose a deep learning based method for segmenting a facade into\n\n  semantic categories. Man-made structures often present the characteristic of\n\n  symmetry. Based on this observation, we propose a symmetric regularizer for\n\n  training the neural network. Our proposed method can make use of both the\n\n  power of deep neural networks and the structure of man-made architectures. We\n\n  also propose a method to refine the segmentation results using bounding boxes\n\n  generated by the Region Proposal Network. We test our method by training a\n\n  FCN-8s network with the novel loss function. Experimental results show that\n\n  our method has outperformed previous state-of-the-art methods significantly on\n\n  both the ECP dataset and the eTRIMS dataset. As far as we know, we are the\n\n  first to employ end-to-end deep convolutional neural network on full image\n\n  scale in the task of building facades parsing.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/320","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"2301-2307","source":"Crossref","is-referenced-by-count":43,"title":["DeepFacade: A Deep Learning Approach to Facade Parsing"],"prefix":"10.24963","author":[{"given":"Hantang","family":"Liu","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianke","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven C. H.","family":"Hoi","sequence":"additional","affiliation":[{"name":"School of Information Systems, SMU, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"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":"2017-07-28T11:53:21Z","timestamp":1501242801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/320"}},"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\/320","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}