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Real-world data often follow a long-tailed distribution as the frequency of each class is typically different.
Ting, K.M.: A comparative study of cost-sensitive boosting algorithms. In: ICML (2000)
2000
Earlier work this paper cites.
Elkan, C.: The foundations of cost-sensitive learning (2001)
2001
Earlier work this paper cites.
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. JAIR (2002)
2002
Earlier work this paper cites.
Drummond, C., Holte, R.C., et al.: C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Workshop on learning from imbalanced datasets II (2003)
2003
Earlier work this paper cites.
Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. TKDE (2005)
2005
Earlier work this paper cites.
He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (2008)
2008
Earlier work this paper cites.
2008
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Tech. rep., Citeseer (2009)
2009
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV (2010)
2010
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
2012
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV (2014)
2014
Earlier work this paper cites.
Akata, Z., Reed, S., Walter, D., Lee, H., Schiele, B.: Evaluation of output embeddings for fine-grained image classification. In: CVPR (2015)
2015
Earlier work this paper cites.
Bengio, S.: Sharing representations for long tail computer vision problems. In: ICMI (2015)
2015
Earlier work this paper cites.
Chen, C., Seff, A., Kornhauser, A., Xiao, J.: Deepdriving: Learning affordance for direct perception in autonomous driving. In: ICCV (2015)
2015
Earlier work this paper cites.
Girshick, R.: Fast r-cnn. In: ICCV (2015)
2015
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. IJCV (2015)
2015
Earlier work this paper cites.
Bian, X., Lim, S.N., Zhou, N.: Multiscale fully convolutional network with application to industrial inspection. In: WACV (2016)
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Cited alongside, same era.
Huang, C., Li, Y., Change Loy, C., Tang, X.: Learning deep representation for imbalanced classification. In: CVPR (2016)
2016
Cited alongside, same era.
Oh Song, H., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: CVPR (2016)
2016
Cited alongside, same era.
Ouyang, W., Wang, X., Zhang, C., Yang, X.: Factors in finetuning deep model for object detection with long-tail distribution. In: CVPR (2016)
2016
Cited alongside, same era.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: CVPR (2018)
2018
Later among the works it cites.
Sarafianos, N., Xu, X., Kakadiaris, I.A.: Deep imbalanced attribute classification using visual attention aggregation. In: ECCV (2018)
2018
Later among the works it cites.
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: The inaturalist species classification and detection dataset. In: CVPR (2018)
2018
Later among the works it cites.
Wang, Y.X., Girshick, R., Hebert, M., Hariharan, B.: Low-shot learning from imaginary data. In: CVPR (2018)
2018
Later among the works it cites.
Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: Feature generating networks for zero-shot learning. In: CVPR (2018)
2018
Later among the works it cites.
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Shen, L., Lin, Z., Huang, Q.: Relay backpropagation for effective learning of deep convolutional neural networks. In: ECCV (2016)
2016
Cited alongside, same era.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)
2016
Cited alongside, same era.
Geifman, Y., El-Yaniv, R.: Deep active learning over the long tail. arXiv:1711.00941 (2017)
2017
Cited alongside, same era.
Hariharan, B., Girshick, R.: Low-shot visual recognition by shrinking and hallucinating features. In: CVPR (2017)
2017
Cited alongside, same era.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)
2017
Cited alongside, same era.
Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. TNNLS (2017)
2017
Cited alongside, same era.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV (2017)
2017
Cited alongside, same era.
2018
Later among the works it cites.
Zhu, P., Wen, L., Du, D., Bian, X., Ling, H., Hu, Q., Wu, H., Nie, Q., Cheng, H., Liu, C., et al.: Visdrone-vdt2018: The vision meets drone video detection and tracking challenge results. In: ECCV (2018)
2018
Later among the works it cites.
Chen, Y., Bai, Y., Zhang, W., Mei, T.: Destruction and construction learning for fine-grained image recognition. In: CVPR (2019)
2019
Later among the works it cites.
Chen, Z., Fu, Y., Chen, K., Jiang, Y.G.: Image block augmentation for one-shot learning. In: AAAI (2019)
2019
Later among the works it cites.
Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: CVPR (2019)
2019
Later among the works it cites.
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: CVPR (2019)
2019
Later among the works it cites.
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: ICML (2019)
2019
Later among the works it cites.
Kang, B., Xie, S., Rohrbach, M., Yan, Z., Gordo, A., Feng, J., Kalantidis, Y.: Decoupling representation and classifier for long-tailed recognition. In: ICLR (2020)
2020
Closest in time.
Zhou, B., Cui, Q., Wei, X.S., Chen, Z.M.: Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition. In: CVPR (2020)
2020
Closest in time.