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Appl."],"published-print":{"date-parts":[[2019,11,30]]},"abstract":"<jats:p>In this article, we propose a novel method to address the two-dimensional (2D) image-based 3D object retrieval problem. First, we extract a set of virtual views to represent each 3D object. Then, a soft-attention model is utilized to find the weight of each view to select one characteristic view for each 3D object. Second, we propose a novel Holistic Generative Adversarial Network (HGAN) to solve the cross-domain feature representation problem and make the feature space of virtual characteristic view more inclined to the feature space of the real picture. This will effectively mitigate the distribution discrepancies across the 2D image domains and 3D object domains. Finally, we utilize the generative model of the HGAN to obtain the \u201cvirtual real image\u201d of each 3D object and make the characteristic view of the 3D object and real picture possess the same feature space for retrieval. To demonstrate the performance of our approach, We established a new dataset that includes pairs of 2D images and 3D objects, where the 3D objects are based on the ModelNet40 dataset. The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3344684","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T13:12:30Z","timestamp":1576501950000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["HGAN"],"prefix":"10.1145","volume":"15","author":[{"given":"Weizhi","family":"Nie","sequence":"first","affiliation":[{"name":"Tianjin University, TianJin, China"}]},{"given":"Weijie","family":"Wang","sequence":"additional","affiliation":[{"name":"Tianjin University, TianJin, China"}]},{"given":"Anan","family":"Liu","sequence":"additional","affiliation":[{"name":"Tianjin University, TianJin, China"}]},{"given":"Jie","family":"Nie","sequence":"additional","affiliation":[{"name":"Ocean University of China"}]},{"given":"Yuting","family":"Su","sequence":"additional","affiliation":[{"name":"Tianjin University, TianJin, China"}]}],"member":"320","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the Eurographics Workshop on 3D Object Retrieval, Alex Telea, Theoharis Theoharis, and Remco Veltkamp (Eds.). 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In Computer Vision and Pattern Recognition. 3762--3769.","DOI":"10.1109\/CVPR.2014.487"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the IEEE International Conference on Computer Vision. 2875--2883","author":"Aubry Mathieu","unstructured":"Mathieu Aubry and Bryan C. Russell . 2015. Understanding deep features with computer-generated imagery . In Proceedings of the IEEE International Conference on Computer Vision. 2875--2883 . Mathieu Aubry and Bryan C. Russell. 2015. Understanding deep features with computer-generated imagery. In Proceedings of the IEEE International Conference on Computer Vision. 2875--2883."},{"key":"e_1_2_1_6_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. Comput. Sci. (2014).  Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. Comput. Sci. (2014)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2007.09.001"},{"key":"e_1_2_1_8_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho , Bart Van Merrienboer , Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 . Learning phrase representations using RNN encoder-decoder for statistical machine translation. Comput. Sci . (2014). Kyunghyun Cho, Bart Van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Comput. Sci. 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In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2536--2544 . Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros. 2016. Context encoders: Feature learning by inpainting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2536--2544."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71589-6_9"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366155"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-007-0097-8"},{"key":"e_1_2_1_35_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. 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Jiajun Wu Chengkai Zhang Tianfan Xue Bill Freeman and Josh Tenenbaum. 2016. Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In Advances in Neural Information Processing Systems. 82--90."},{"key":"e_1_2_1_43_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1912--1920","author":"Wu Zhirong","year":"2014","unstructured":"Zhirong Wu , S. Song , A. Khosla , and Fisher Yu . 2014 . 3D ShapeNets: A deep representation for volumetric shapes . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1912--1920 . Zhirong Wu, S. Song, A. Khosla, and Fisher Yu. 2014. 3D ShapeNets: A deep representation for volumetric shapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1912--1920."},{"key":"e_1_2_1_44_1","volume-title":"International Conference on Machine Learning. 2048--2057","author":"Xu Kelvin","year":"2015","unstructured":"Kelvin Xu , Jimmy Ba , Ryan Kiros , Kyunghyun Cho , Aaron Courville , Ruslan Salakhudinov , Rich Zemel , and Yoshua Bengio . 2015 . Show, attend and tell: Neural image caption generation with visual attention . In International Conference on Machine Learning. 2048--2057 . Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning. 2048--2057."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti458"},{"key":"e_1_2_1_46_1","volume-title":"Proceedings of the International Conference on Neural Information Processing Systems. 1376--1384","author":"Yi Zhen","year":"2012","unstructured":"Zhen Yi and Dit Yan Yeung . 2012 . Co-regularized hashing for multimodal data . In Proceedings of the International Conference on Neural Information Processing Systems. 1376--1384 . Zhen Yi and Dit Yan Yeung. 2012. Co-regularized hashing for multimodal data. In Proceedings of the International Conference on Neural Information Processing Systems. 1376--1384."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2608906"},{"key":"e_1_2_1_48_1","volume-title":"Recurrent neural network regularization. arXiv preprint arXiv:1409.2329","author":"Zaremba Wojciech","year":"2014","unstructured":"Wojciech Zaremba , Ilya Sutskever , and Oriol Vinyals . 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 ( 2014 ). Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.547"},{"key":"e_1_2_1_50_1","volume-title":"Proceedings of the Association for the Advancement of Artificial Intelligence Conference (AAAI\u201916)","author":"Zhu Fan","year":"2016","unstructured":"Fan Zhu , Jin Xie , and Yi Fang . 2016 . 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