{"type":"ScholarlyArticle","title":"Landmark Selection for Zero-shot Learning","authors":[{"name":"Yuchen Guo","slug":"yuchen-guo","given":"Yuchen","family":"Guo","affiliation":"Department of Automation , Tsinghua University , Beijing , China"},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":"School of Software , Tsinghua University , Beijing , China"},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":"WMG Data Science , University of Warwick , Coventry , UK"},{"name":"Chenggang Yan","slug":"chenggang-yan","given":"Chenggang","family":"Yan","affiliation":"School of Automation , Hangzhou Dianzi University , China"},{"name":"Jiyong Zhang","slug":"jiyong-zhang","given":"Jiyong","family":"Zhang","affiliation":"School of Automation , Hangzhou Dianzi University , China"},{"name":"Qionghai Dai","slug":"qionghai-dai","given":"Qionghai","family":"Dai","affiliation":"Department of Automation , Tsinghua University , Beijing , China"}],"abstract":"Zero-shot learning (ZSL) is an emerging research topic whose goal is to build recognition models for previously unseen classes. The basic idea of ZSL is based on heterogeneous feature matching which learns a compatibility function between image and class features using seen classes. The function is constructed based on one-vs-all training in which each class has only one class feature and many image features. Existing ZSL works mostly treat all image features equivalently. However, in this paper we argue that it is more reasonable to use some representative cross-domain data instead of all. Motivated by this idea, we propose a novel approach, termed as Landmark Selection(LAST) for ZSL. LAST is able to identify representative cross-domain features which further lead to better image-class compatibility function. Experiments on several ZSL datasets including ImageNet demonstrate the superiority of LAST to the state-of-the-arts.","doi":"10.24963/ijcai.2019/338","url":"https://ijcai.org/proceedings/2019/338","pdf":"https://ijcai.org/proceedings/2019/0338.pdf","markdown":"https://ijcai.org/proceedings/2019/338.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/338","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":338,"pages":{"first":2435,"last":2441},"keywords":["Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Machine Learning: Transfer, Adaptation, Multi-task Learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2014] Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and Philip S. Yu. Active learning: A survey. In Data Classification: Algorithms and Applications, pages 571-606. 2014.","title":"Active learning: A survey","authors":"C Charu, Xiangnan Aggarwal, Quanquan Kong, Jiawei Gu, Philip S Han, Yu","year":"2014","venue":"Data Classification: Algorithms and Applications","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Akata et al., 2015] Zeynep Akata, Scott E. Reed, Daniel Walter, Honglak Lee, and Bernt Schiele. Evaluation of output embed- dings for fine-grained image classification. In CVPR, 2015.","title":"Evaluation of output embeddings for fine-grained image classification","authors":"Zeynep Akata, Scott E Reed, Daniel Walter, Honglak Lee, Bernt Schiele","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Akata et al., 2016] Zeynep Akata, Florent Perronnin, Zaïd Har- chaoui, and Cordelia Schmid. Label-embedding for image clas- sification. IEEE TPAMI, 2016.","title":"Label-embedding for image classification","authors":"Zeynep Akata, Florent Perronnin, Zaïd Harchaoui, Cordelia Schmid","year":"2016","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Annadani and Biswas, 2018] Yashas Annadani and Soma Biswas. Preserving semantic relations for zero-shot learning. In CVPR, 2018.","title":"Yashas Annadani and Soma Biswas. Preserving semantic relations for zero-shot learning","authors":"Biswas Annadani","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bartels and Stewart, 1972] Richard H. Bartels and G. W. Stewart. Solution of the matrix equation ax+xb=c [F4] (algorithm 432). Commun. ACM, 15(9):820-826, 1972.","title":"Solution of the matrix equation ax+xb=c [F4] (algorithm 432)","authors":"Richard H Bartels, G W Stewart","year":"1972","venue":"Commun. ACM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Changpinyo et al., 2016] Soravit Changpinyo, Wei-Lun Chao, Bo- qing Gong, and Fei Sha. Synthesized classifiers for zero-shot learning. In CVPR, 2016.","title":"Synthesized classifiers for zero-shot learning","authors":"Changpinyo","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2018] Long Chen, Hanwang Zhang, Jun Xiao, Wei Liu, and Shih-Fu Chang. Zero-shot visual recognition using semantics-preserving adversarial embedding networks. In CVPR, 2018.","title":"Zero-shot visual recognition using semantics-preserving adversarial embedding networks","authors":"Long Chen, Hanwang Zhang, Jun Xiao, Wei Liu, Shih-Fu Chang","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Farhadi et al., 2009] Ali Farhadi, Ian Endres, Derek Hoiem, and David A. Forsyth. Describing objects by their attributes. In CVPR, 2009.","title":"Describing objects by their attributes","authors":"Ali Farhadi, Ian Endres, Derek Hoiem, David A Forsyth","year":"2009","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Felix et al., 2018] Rafael Felix, B. G. Vijay Kumar, Ian D. Reid, and Gustavo Carneiro. Multi-modal cycle-consistent generalized zero-shot learning. In ECCV, 2018.","title":"Multi-modal cycle-consistent generalized zero-shot learning","authors":"Rafael Felix, B G Vijay Kumar, Ian D Reid, Gustavo Carneiro","year":"2018","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Frome et al., 2013] Andrea Frome, Gregory S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. In NIPS, 2013.","title":"Devise: A deep visual-semantic embedding model","authors":"Andrea Frome, Gregory S Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'aurelio Ranzato, Tomas Mikolov","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fu et al., 2015] Zhenyong Fu, Tao Xiang, Elyor Kodirov, and Shaogang Gong. Zero-shot object recognition by semantic man- ifold distance. In CVPR, 2015.","title":"Zero-shot object recognition by semantic manifold distance","authors":"Fu","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Guo et al., 2016] Yuchen Guo, Guiguang Ding, Xiaoming Jin, and Jianmin Wang. Transductive zero-shot recognition via shared model space learning. In AAAI, 2016.","title":"Transductive zero-shot recognition via shared model space learning","authors":"Yuchen Guo, Guiguang Ding, Xiaoming Jin, Jianmin Wang","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Guo et al., 2017a] Yuchen Guo, Guiguang Ding, Jungong Han, and Yue Gao. Synthesizing samples for zero-shot learning. In IJCAI, 2017.","title":"Synthesizing samples for zero-shot learning","authors":"Guo","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/246"},{"position":14,"raw":"[Guo et al., 2017b] Yuchen Guo, Guiguang Ding, Jungong Han, and Yue Gao. Zero-shot learning with transferred samples. IEEE TIP, 26(7):3277-3290, 2017.","title":"Zero-shot learning with transferred samples","authors":"Guo","year":"2017","venue":"IEEE TIP","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Guo et al., 2018] Yuchen Guo, Guiguang Ding, Jungong Han, Sicheng Zhao, and Bin Wang. Implicit non-linear similarity s- coring for recognizing unseen classes. In IJCAI, 2018.","title":"Implicit non-linear similarity scoring for recognizing unseen classes","authors":"Yuchen Guo, Guiguang Ding, Jungong Han, Sicheng Zhao, Bin Wang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/680"},{"position":16,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Huang et al., 2012] Eric H. Huang, Richard Socher, Christo- pher D. Manning, and Andrew Y. Ng. Improving word repre- sentations via global context and multiple word prototypes. In ACL, 2012.","title":"Improving word representations via global context and multiple word prototypes","authors":"Eric H Huang, Richard Socher, Christopher D Manning, Andrew Y Ng","year":"2012","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kodirov et al., 2017] Elyor Kodirov, Tao Xiang, and Shaogang Gong. Semantic autoencoder for zero-shot learning. In CVPR, 2017.","title":"Semantic autoencoder for zero-shot learning","authors":"Kodirov","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lampert et al., 2014] Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling. Attribute-based classification for zero- shot visual object categorization. IEEE TPAMI, 2014.","title":"Attribute-based classification for zeroshot visual object categorization","authors":"Christoph H Lampert, Hannes Nickisch, Stefan Harmeling","year":"2014","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Norouzi et al., 2013] Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, and Jeffrey Dean. Zero-shot learning by convex combi- nation of semantic embeddings. CoRR, abs/1312.5650, 2013.","title":"Zero-shot learning by convex combination of semantic embeddings","authors":"Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, Jeffrey Dean","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Pan and Yang, 2010] Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE TKDE, 2010.","title":"A survey on transfer learning","authors":"Jialin Sinno, Qiang Pan, Yang","year":"2010","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Patterson and Hays, 2012] Genevieve Patterson and James Hays. SUN attribute database: Discovering, annotating, and recogniz- ing scene attributes. In CVPR, 2012.","title":"SUN attribute database: Discovering, annotating, and recognizing scene attributes","authors":"Genevieve Patterson, James Hays","year":"2012","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Romera-Paredes and Torr, 2015] Bernardino Romera-Paredes and Philip H. S. Torr. An embarrassingly simple approach to zero- shot learning. In ICML, 2015.","title":"An embarrassingly simple approach to zeroshot learning","authors":"Romera-Paredes, Torr ; Bernardino Romera-Paredes, Philip H S Torr","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Russakovsky et al., 2015] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z.g Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and Fei-Fei Li. Ima- genet large scale visual recognition challenge. IJCV, 2015.","title":"Imagenet large scale visual recognition challenge","authors":"O Russakovsky, J Deng, H Su, J Krause, S Satheesh, S Ma, Z Huang, A Karpathy, A Khosla, M S Bernstein, A C Berg, Fei-Fei Li","year":"2015","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Shrivastava et al., 2016] Abhinav Shrivastava, Abhinav Gupta, and Ross B. Girshick. Training region-based object detectors with online hard example mining. In CVPR, 2016.","title":"Training region-based object detectors with online hard example mining","authors":"Abhinav Shrivastava, Abhinav Gupta, Ross B Girshick","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Socher et al., 2013] Richard Socher, Milind Ganjoo, Christo- pher D. Manning, and Andrew Y. Ng. Zero-shot learning through cross-modal transfer. In NIPS, 2013.","title":"Zero-shot learning through cross-modal transfer","authors":"Richard Socher, Milind Ganjoo, Christopher D Manning, Andrew Y Ng","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Tsai et al., 2016] Yao-Hung Hubert Tsai, Yi-Ren Yeh, and Yu- Chiang Frank Wang. Learning cross-domain landmarks for het- erogeneous domain adaptation. In CVPR, 2016.","title":"Learning cross-domain landmarks for heterogeneous domain adaptation","authors":"Yao-Hung Hubert Tsai, Yi-Ren Yeh, Yu-Chiang Frank, Wang","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wah et al., 2011] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The Caltech-UCSD Birds- 200-2011 Dataset. Technical Report CNS-TR-2011-001, Cali- fornia Institute of Technology, 2011.","title":"The Caltech-UCSD Birds-200-2011 Dataset","authors":"Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, Serge Belongie","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang and Deng, 2018] Mei Wang and Weihong Deng. Deep visu- al domain adaptation: A survey. Neurocomputing, 2018.","title":"Deep visual domain adaptation: A survey","authors":"Mei Wang, Weihong Deng","year":"2018","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Xian et al., 2016] Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh N. Nguyen, Matthias Hein, and Bernt Schiele. Latent embeddings for zero-shot classification. In CVPR, 2016.","title":"Latent embeddings for zero-shot classification","authors":"Xian","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Xian et al., 2017] Yongqin Xian, Christoph H. Lampert, Bern- t Schiele, and Zeynep Akata. Zero-shot learning -A compre- hensive evaluation of the good, the bad and the ugly. CoRR, ab- s/1707.00600, 2017.","title":"Zero-shot learning -A comprehensive evaluation of the good, the bad and the ugly","authors":"Xian","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ye and Guo, 2017] Meng Ye and Yuhong Guo. Zero-shot classi- fication with discriminative semantic representation learning. In CVPR, 2017.","title":"Zero-shot classification with discriminative semantic representation learning","authors":"Meng Ye, Yuhong Guo","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zhang and Koniusz, 2018] Hongguang Zhang and Piotr Koniusz. Zero-shot kernel learning. In CVPR, 2018.","title":"Zero-shot kernel learning","authors":"Hongguang Zhang, Piotr Koniusz","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zhang and Saligrama, 2015] Ziming Zhang and Venkatesh Saligrama. Zero-shot learning via semantic similarity embed- ding. In ICCV, 2015.","title":"Zero-shot learning via semantic similarity embedding","authors":"Ziming Zhang, Venkatesh Saligrama","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zhang and Saligrama, 2016] Ziming Zhang and Venkatesh Saligrama. Zero-shot learning via joint latent similarity embedding. In CVPR, 2016.","title":"Zero-shot learning via joint latent similarity embedding","authors":"Ziming Zhang, Venkatesh Saligrama","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}