References(31)
[1]
Kanamori, Y.; Yamada, H.; Hirose, M.; Mitani, J.; Fukui, Y. Image-based virtual try-on system with garment reshaping and color correction. In: Lecture Notes in Computer Science, Vol. 9550. Gavrilova, M.; Tan, C.; Iglesias, A.; Shinya, M.; Galvez, A.; Sourin, A. Eds. Berlin, Heidelberg: Springer, 1-16, 2016.
[2]
Di, W.; Wah, C.; Bhardwaj, A.; Piramuthu, R.; Sundaresan, N. Style finder: Fine-grained clothing style detection and retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 8-13, 2013.
[3]
Hu, Y.; Yi, X.; Davis, L. S. Collaborative fashion recommendation: A functional tensor factorization approach. In: Proceedings of the 23rd ACM International Conference on Multimedia, 129-138, 2015.
[4]
Kalantidis, Y.; Kennedy, L.; Li, L.-J. Getting the look: Clothing recognition and segmentation for automatic product suggestions in everyday photos. In: Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, 105-112, 2013.
[5]
Wei, S.-E.; Ramakrishna, V.; Kanade, T.; Sheikh, Y. Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4724-4732, 2016.
[6]
Liang, X.; Xu, C.; Shen, X.; Yang, J.; Tang, J.; Lin, L.; Yan, S. Human parsing with contextualized convolutional neural network. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 39, No. 1, 115-127, 2017.
[7]
Quattoni, A.; Torralba, A. Recognizing indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 413-420, 2009.
[8]
Yamaguchi, K.; Kiapour, M. H.; Ortiz, L. E.; Berg, T. L. Parsing clothing in fashion photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3570-3577, 2012.
[9]
Yamaguchi, K.; Kiapour, M.; Ortiz, L.; Berg, T. Retrieving similar styles to parse clothing. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 37, No. 5, 1028-1040, 2015.
[10]
Simo-Serra, E.; Fidler, S.; Moreno-Noguer, F.; Urtasun, R. A high performance CRF model for clothes parsing. In: Proceedings of the Asian Conference on Computer Vision, 64-81, 2014.
[11]
Dong, J.; Chen, Q.; Shen, X.; Yang, J.; Yan, S. Towards unified human parsing and pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 843-850, 2014.
[12]
Liu, S.; Liang, X.; Liu, L.; Lu, K.; Lin, L.; Yan, S. Fashion parsing with video context. In: Proceedings of the 22nd ACM International Conference on Multimedia, 467-476, 2014.
[13]
Liang, X.; Liu, S.; Shen, X.; Yang, J.; Liu, L.; Dong, J.; Lin, L.; Yan, S. Deep human parsing with active template regression. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 37, No. 12, 2402-2414, 2015.
[14]
Liu, S.; Liang, X.; Liu, L.; Shen, X.; Yang, J.; Xu, C.; Lin, L.; Cao, X.; Yan, S. Matching-CNN meets KNN: Quasi-parametric human parsing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1419-1427, 2015.
[15]
Bertasius, G.; Shi, J.; Torresani, L. Semantic segmentation with boundary neural fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3602-3610, 2016.
[16]
Ghiasi, G.; Fowlkes, C. C. Laplacian pyramid reconstruction and refinement for semantic segmentation. In: Proceedings of the European Conference on Computer Vision, 519-534, 2016.
[17]
Liang, X.; Shen, X.; Feng, J.; Lin, L.; Yan, S. Semantic object parsing with graph LSTM. In: Proceedings of the European Conference on Computer Vision, 125-143, 2016.
[18]
Liang, X.; Shen, X.; Xiang, D.; Feng, J.; Lin, L.; Yan, S. Semantic object parsing with local-global long short-term memory. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3185-3193, 2016.
[19]
Lin, G.; Shen, C.; van den Hengel, A.; Reid, I. Efficient piecewise training of deep structured models for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3194-3203, 2016.
[20]
Vemulapalli, R.; Tuzel, O.; Liu, M.-Y.; Chellapa, R. Gaussian conditional random field network for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3224-3233, 2016.
[21]
Dai, J.; He, K.; Sun, J. Instance-aware semantic segmentation via multi-task network cascades. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3150-3158, 2016.
[22]
Hong, S.; Oh, J.; Lee, H.; Han, B. Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3204-3212, 2016.
[23]
Papandreou, G.; Chen, L.; Murphy, K. P.; Yuille, A. L. Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, 1742-1750, 2015.
[24]
Yang, W.; Ouyang, W.; Li, H.; Wang, X. End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3073-3082, 2016.
[25]
Chu, X.; Ouyang, W.; Li, H.; Wang, X. Structured feature learning for pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4715-4723, 2016.
[26]
Andriluka, M.; Pishchulin, L.; Gehler, P.; Schiele, B. 2D human pose estimation: New benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3686-3693, 2014.
[27]
Aksoy, Y.; Aydin, T. O.; Pollefeys, M. Designing effective inter-pixel information flow for natural image matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 29-37, 2017.
[28]
Floater, M. S. Mean value coordinates. Computer Aided Geometric Design Vol. 20, No. 1, 19-27, 2003.
[29]
Van der Maaten, L.; Hinton, G. Visualizing data using t-SNE. Journal of Machine Learning Research Vol. 9, 2579-2605, 2008.
[30]
Simo-Serra, E.; Ishikawa, H. Fashion style in 128 floats: Joint ranking and classification using weak data for feature extraction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 298-307, 2016.
[31]
He, H.; Bai, Y.; Garcia, E. A.; Li, S. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322-1328, 2008.