|
[1] M. H. Kiapour, K. Yamaguchi, A. C. Berg, and T. L. Berg, “Hipster wars: Discovering elements of fashion styles,” in European conference on computer vision, pp. 472–488, Springer, 2014. [2] M. Takagi, E. Simo-Serra, S. Iizuka, and H. Ishikawa, “What makes a style: Experimental analysis of fashion prediction,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2247–2253, 2017. [3] Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang, “Deepfashion: Powering robust clothes recognition and retrieval with rich annotations,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1096–1104, 2016. [4] F. Perronnin and C. Dance, “Fisher kernels on visual vocabularies for image categorization,” in 2007 IEEE conference on computer vision and pattern recognition, pp. 1–8, IEEE, 2007. [5] F. Perronnin, J. S ́anchez, and T. Mensink, “Improving the fisher kernel for large-scale image classification,” in European conference on computer vision, pp. 143–156, Springer, 2010. [6] H. J ́egou, M. Douze, C. Schmid, and P. P ́erez, “Aggregating local descriptors into acompact image representation,” in2010 IEEE computer society conference on computer vision and pattern recognition, pp. 3304–3311, IEEE, 2010. [7] G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Workshop on statistical learning in computer vision, ECCV, vol. 1, pp. 1–2, Prague, 2004. [8] J. Sivic and A. Zisserman, “Video google: A text retrieval approach to object matching in videos,” in null, p. 1470, IEEE, 2003. [9] M. Cimpoi, S. Maji, and A. Vedaldi, “Deep filter banks for texture recognition and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3828–3836, 2015. [10] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, pp. 1097–1105, 2012. [11] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014. [12] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015. [13] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778, 2016. [14] A. Coates and A. Y. Ng, “Learning feature representations with k-means,” in Neural networks: Tricks of the trade, pp. 561–580, Springer, 2012. [15] J. Yang, D. Parikh, and D. Batra, “Joint unsupervised learning of deep representations and image clusters,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5147–5156, 2016. [16] J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in International conference on machine learning, pp. 478–487, 2016. [17] M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 132–149, 2018. [18] L. Sharan, R. Rosenholtz, and E. Adelson, “Material perception: What can you see in a brief glance?,” Journal of Vision, vol. 9, no. 8, pp. 784–784, 2009. [19] M. Fritz, E. Hayman, B. Caputo, and J.-O. Eklundh, “The kth-tips database,” 2004.
|