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Existing approaches for unsupervised metric learning focus on exploring self-supervision information within the input image itself.
Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Tech. rep., Citeseer (2002)
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Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). vol. 1, pp. 539–546. IEEE (2005)
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Zhu, X.J.: Semi-supervised learning literature survey. Tech. rep., University of Wisconsin-Madison Department of Computer Sciences (2005)
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Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06). vol. 2, pp. 1735–1742. IEEE (2006)
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Gazzaniga, M.S.: The cognitive neurosciences. MIT Press (2009)
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2014
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Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in neural information processing systems. pp. 2017–2025 (2015)
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115
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Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 815–823 (2015)
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Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1–9 (2015)
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Wohlhart, P., Lepetit, V.: Learning descriptors for object recognition and 3d pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3109–3118 (2015)
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Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: European Conference on Computer Vision. pp. 69–84. Springer (2016)
2016
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Oh Song, H., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4004–4012 (2016)
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Grabner, A., Roth, P.M., Lepetit, V.: 3d pose estimation and 3d model retrieval for objects in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3022–3031 (2018)
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He, X., Zhou, Y., Zhou, Z., Bai, S., Bai, X.: Triplet-center loss for multi-view 3d object retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1945–1954 (2018)
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Iscen, A., Tolias, G., Avrithis, Y., Chum, O.: Mining on manifolds: Metric learning without labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7642–7651 (2018)
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Kim, W., Goyal, B., Chawla, K., Lee, J., Kwon, K.: Attention-based ensemble for deep metric learning. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 736–751 (2018)
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Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: Advances in neural information processing systems. pp. 1857–1865 (2016)
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Tao, R., Gavves, E., Smeulders, A.W.: Siamese instance search for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1420–1429 (2016)
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Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: European conference on computer vision. pp. 499–515. Springer (2016)
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2017
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Fu, J., Zheng, H., Mei, T.: Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4438–4446 (2017)
2017
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2017
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2017
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Movshovitz-Attias, Y., Toshev, A., Leung, T.K., Ioffe, S., Singh, S.: No fuss distance metric learning using proxies. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 360–368 (2017)
2017
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Opitz, M., Waltner, G., Possegger, H., Bischof, H.: Deep metric learning with bier: Boosting independent embeddings robustly. IEEE transactions on pattern analysis and machine intelligence (2018)
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Woo, S., Park, J., Lee, J.Y., So Kweon, I.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 3–19 (2018)
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Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3733–3742 (2018)
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Yu, R., Dou, Z., Bai, S., Zhang, Z., Xu, Y., Bai, X.: Hard-aware point-to-set deep metric for person re-identification. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 188–204 (2018)
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Chen, B., Deng, W.: Hybrid-attention based decoupled metric learning for zero-shot image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2750–2759 (2019)
2019
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2019
Later among the works it cites.
Wang, X., Han, X., Huang, W., Dong, D., Scott, M.R.: Multi-similarity loss with general pair weighting for deep metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5022–5030 (2019)
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2019
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Ye, M., Zhang, X., Yuen, P.C., Chang, S.F.: Unsupervised embedding learning via invariant and spreading instance feature. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6210–6219 (2019)
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Zhang, L., Qi, G.J., Wang, L., Luo, J.: Aet vs. aed: Unsupervised representation learning by auto-encoding transformations rather than data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2547–2555 (2019)
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