{"type":"ScholarlyArticle","title":"Group-wise Deep Co-saliency Detection","authors":[{"name":"Lina Wei","slug":"lina-wei","given":"Lina","family":"Wei","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Shanshan Zhao","slug":"shanshan-zhao","given":"Shanshan","family":"Zhao","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Omar El Farouk Bourahla","slug":"omar-el-farouk-bourahla","given":"Omar El Farouk","family":"Bourahla","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Xi Li","slug":"xi-li","given":"Xi","family":"Li","affiliation":"Zhejiang University , Hangzhou , China; Alibaba-Zhejiang University Joint Institute of Frontier Technologies , Hangzhou , China"},{"name":"Fei Wu","slug":"fei-wu","given":"Fei","family":"Wu","affiliation":"Zhejiang University , Hangzhou , China"}],"abstract":"In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output. The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single-image individual feature representation, and model this in a collaborative learning framework. Finally, we set up a unified end-to-end deep learning scheme to jointly optimize the process of group-wise feature representation learning and the collaborative learning, leading to more reliable and robust co-saliency detection results. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.","doi":"10.24963/ijcai.2017/424","url":"https://ijcai.org/proceedings/2017/424","pdf":"https://ijcai.org/proceedings/2017/0424.pdf","markdown":"https://ijcai.org/proceedings/2017/424.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/424","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":424,"pages":{"first":3041,"last":3047},"keywords":["Machine Learning: Neural Networks","Robotics and Vision: Vision and Perception"],"videos":[],"references":[{"position":1,"raw":"[Batra et al., 2010] Dhruv Batra, Adarsh Kowdle, Devi Parikh, Jiebo Luo, and Tsuhan Chen. icoseg: Interac- tive co-segmentation with intelligent scribble guidance. 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