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In CVPR. 13480--13489.","DOI":"10.1109\/CVPR42600.2020.01349"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3324926"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3045079"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"crossref","unstructured":"Ryota Yoshihashi Wen Shao Rei Kawakami Shaodi You Makoto Iida and Takeshi Naemura. 2019. Classification-reconstruction learning for open-set recognition. In CVPR. 4016--4025. Ryota Yoshihashi Wen Shao Rei Kawakami Shaodi You Makoto Iida and Takeshi Naemura. 2019. Classification-reconstruction learning for open-set recognition. In CVPR. 4016--4025.","DOI":"10.1109\/CVPR.2019.00414"},{"key":"e_1_3_2_2_64_1","volume-title":"Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365","author":"Yu Fisher","year":"2015","unstructured":"Fisher Yu , Ari Seff , Yinda Zhang , Shuran Song , Thomas Funkhouser , and Jianxiong Xiao . 2015 . Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015). Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. 2015. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)."},{"key":"e_1_3_2_2_65_1","volume-title":"Attribute-based transfer learning for object categorization with zero\/one training example","author":"Yu Xiaodong","unstructured":"Xiaodong Yu and Yiannis Aloimonos . 2010. Attribute-based transfer learning for object categorization with zero\/one training example . In ECCV. Springer , 127--140. Xiaodong Yu and Yiannis Aloimonos. 2010. Attribute-based transfer learning for object categorization with zero\/one training example. In ECCV. Springer, 127--140."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"crossref","unstructured":"Yang Yu Wei-Yang Qu Nan Li and Zimin Guo. 2017. Open-category classification by adversarial sample generation. In IJCAI. 3357--3363. Yang Yu Wei-Yang Qu Nan Li and Zimin Guo. 2017. Open-category classification by adversarial sample generation. In IJCAI. 3357--3363.","DOI":"10.24963\/ijcai.2017\/469"},{"key":"e_1_3_2_2_67_1","unstructured":"Zhongqi Yue Tan Wang Qianru Sun Xian-Sheng Hua and Hanwang Zhang. 2021. Counterfactual zero-shot and open-set visual recognition. In CVPR. 15404--15414. Zhongqi Yue Tan Wang Qianru Sun Xian-Sheng Hua and Hanwang Zhang. 2021. Counterfactual zero-shot and open-set visual recognition. In CVPR. 15404--15414."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2613924"},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"crossref","unstructured":"Li Zhang Tao Xiang and Shaogang Gong. 2017. Learning a deep embedding model for zero-shot learning. In CVPR. 2021--2030. Li Zhang Tao Xiang and Shaogang Gong. 2017. Learning a deep embedding model for zero-shot learning. In CVPR. 2021--2030.","DOI":"10.1109\/CVPR.2017.321"},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"crossref","unstructured":"Ziming Zhang and Venkatesh Saligrama. 2016a. Zero-shot learning via joint latent similarity embedding. In CVPR. 6034--6042. Ziming Zhang and Venkatesh Saligrama. 2016a. Zero-shot learning via joint latent similarity embedding. In CVPR. 6034--6042.","DOI":"10.1109\/CVPR.2016.649"},{"key":"e_1_3_2_2_71_1","volume-title":"Zero-shot recognition via structured prediction","author":"Zhang Ziming","unstructured":"Ziming Zhang and Venkatesh Saligrama . 2016b. Zero-shot recognition via structured prediction . In ECCV. Springer , 533--548. Ziming Zhang and Venkatesh Saligrama. 2016b. Zero-shot recognition via structured prediction. In ECCV. Springer, 533--548."},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"crossref","unstructured":"Da-Wei Zhou Han-Jia Ye and De-Chuan Zhan. 2021b. Learning Placeholders for Open-Set Recognition. In CVPR. 4401--4410. Da-Wei Zhou Han-Jia Ye and De-Chuan Zhan. 2021b. Learning Placeholders for Open-Set Recognition. In CVPR. 4401--4410.","DOI":"10.1109\/CVPR46437.2021.00438"},{"key":"e_1_3_2_2_73_1","volume-title":"Chen Change Loy, and Ziwei Liu","author":"Zhou Kaiyang","year":"2021","unstructured":"Kaiyang Zhou , Jingkang Yang , Chen Change Loy, and Ziwei Liu . 2021 a. Learning to prompt for vision-language models. arXiv preprint arXiv:2109.01134 (2021).io Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2021a. 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