{"type":"ScholarlyArticle","title":"Zero Shot Learning via Low-rank Embedded Semantic AutoEncoder","authors":[{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"},{"name":"Quanxue Gao","slug":"quanxue-gao","given":"Quanxue","family":"Gao","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"},{"name":"Jin Li","slug":"jin-li","given":"Jin","family":"Li","affiliation":"Institute of Artificial Intelligence and Robotics , Xi'an Jiaotong University , Xi'an , China"},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":"School of Computing and Communications , Lancaster University , United Kingdom"},{"name":"Ling Shao","slug":"ling-shao","given":"Ling","family":"Shao","affiliation":"Inception Institute of Artificial Intelligence , Abu Dhabi , United Arab Emirates"}],"abstract":"Zero-shot learning (ZSL) has been widely researched and get successful in machine learning. Most existing ZSL methods aim to accurately recognize objects of unseen classes by learning a shared mapping from the feature space to a semantic space. However, such methods did not investigate in-depth whether the mapping can precisely reconstruct the original visual feature. Motivated by the fact that the data have low intrinsic dimensionality e.g. low-dimensional subspace. In this paper, we formulate a novel framework named Low-rank Embedded Semantic AutoEncoder (LESAE) to jointly seek a low-rank mapping to link visual features with their semantic representations. Taking the encoder-decoder paradigm, the encoder part aims to learn a low-rank mapping from the visual feature to the semantic space, while decoder part manages to reconstruct the original data with the learned mapping. In addition, a non-greedy iterative algorithm is adopted to solve our model. Extensive experiments on six benchmark datasets demonstrate its superiority over several state-of-the-art algorithms.","doi":"10.24963/ijcai.2018/345","url":"https://ijcai.org/proceedings/2018/345","pdf":"https://ijcai.org/proceedings/2018/0345.pdf","markdown":"https://ijcai.org/proceedings/2018/345.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/345","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":"Machine Learning","track":"Main track","number":345,"pages":{"first":2490,"last":2496},"keywords":["Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2013] Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid. 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