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Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection.
Improved baselines with momentum contrastive learning
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Seesaw loss for long-tailed instance segmentation
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Learning multiple layers of features from tiny images
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
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One-shot learning by inverting a compositional causal process
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Microsoft coco: Common objects in context
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Capturing long-tail distributions of object subcategories
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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Relay backpropagation for effective learning of deep convolutional neural networks
Shen, L., Lin, Z., and Huang, Q · 2016
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Matching networks for one shot learning
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Learning to learn: Model regression networks for easy small sample learning
Wang, Y.-X. and Hebert, M · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
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Learning to model the tail
Wang, Y.-X., Ramanan, D., and Hebert, M · 2017
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Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and van der Maaten, L · 2018
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Low-shot learning from imaginary data
Wang, Y.-X., Girshick, R., Hebert, M., and Hariharan, B · 2018
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LVIS: A dataset for large vocabulary instance segmentation
Gupta, A., Dollar, P., and Girshick, R · 2019
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Decoupling representation and classifier for long-tailed recognition
Kang, B., Xie, S., Rohrbach, M., Yan, Z., Gordo, A., Feng, J., and Kalantidis, Y · 2019
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Large-scale long-tailed recognition in an open world
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., and Yu, S. X · 2019
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Detectron2
Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., and Girshick, R · 2019
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Feature transfer learning for face recognition with under-represented data
Yin, X., Yu, X., Sohn, K., Liu, X., and Chandraker, M · 2019
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Momentum contrast for unsupervised visual representation learning
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Unsupervised feature learning via non-parametric instance discrimination
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Metagan: An adversarial approach to few-shot learning
Zhang, R., Che, T., Ghahramani, Z., Bengio, Y., and Song, Y · 2018
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Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
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A closer look at few-shot classification
Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C. F., and Huang, J.-B · 2019
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Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G
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He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Learning to segment the tail
Hu, X., Jiang, Y., Tang, K., Chen, J., Miao, C., and Zhang, H · 2020
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Overcoming classifier imbalance for long-tail object detection with balanced group softmax
Li, Y., Wang, T., Kang, B., Tang, S., Wang, C., Li, J., and Feng, J · 2020
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Equalization loss for long-tailed object recognition
Tan, J., Wang, C., Li, B., Li, Q., Ouyang, W., Yin, C., and Yan, J · 2020
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Forest r-cnn: Large-vocabulary long-tailed object detection and instance segmentation
Wu, J., Song, L., Wang, T., Zhang, Q., and Yuan, J · 2020
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Inflated episodic memory with region self-attention for long-tailed visual recognition
Zhu, L. and Yang, Y · 2020
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