{"type":"ScholarlyArticle","title":"Salient Object Detection by Lossless Feature Reflection","authors":[{"name":"Pingping Zhang","slug":"pingping-zhang","given":"Pingping","family":"Zhang","affiliation":"Dalian University of Technology , Dalian , 116024 , P.R. China; University of Adelaide , Adelaide , SA 5005 , Australia"},{"name":"Wei Liu","slug":"wei-liu","given":"Wei","family":"Liu","affiliation":"Shanghai Jiao Tong University , Shanghai , 200240 , P.R. China; University of Adelaide , Adelaide , SA 5005 , Australia"},{"name":"Huchuan Lu","slug":"huchuan-lu","given":"Huchuan","family":"Lu","affiliation":"Dalian University of Technology , Dalian , 116024 , P.R. China"},{"name":"Chunhua Shen","slug":"chunhua-shen","given":"Chunhua","family":"Shen","affiliation":"University of Adelaide , Adelaide , SA 5005 , Australia"}],"abstract":"Salient object detection, which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new structural loss function to learn clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods.","doi":"10.24963/ijcai.2018/160","url":"https://ijcai.org/proceedings/2018/160","pdf":"https://ijcai.org/proceedings/2018/0160.pdf","markdown":"https://ijcai.org/proceedings/2018/160.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/160","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Computer Vision","track":"Main track","number":160,"pages":{"first":1149,"last":1155},"keywords":["Machine Learning: Deep Learning","Machine Learning: Structured Prediction","Humans and AI: Cognitive Modeling","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Borji et al., 2015] Ali Borji, Ming-Ming Cheng, Huaizu Jiang, and Jia Li. 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