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Detecting rare objects from a few examples is an emerging problem.
The importance of shape in early lexical learning
Landau, B., Smith, L. B., and Jones, S. S · 1988
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Object name learning provides on-the-job training for attention
Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L., and Samuelson, L · 2002
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They call it like they see it: Spontaneous naming and attention to shape
Samuelson, L. K. and Smith, L. B · 2005
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2007
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S. J., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Siamese neural networks for one-shot image recognition
Koch, G · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 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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Low-shot visual recognition by shrinking and hallucinating features
Hariharan, B. and Girshick, R · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Cited alongside, same era.
Low-shot learning with imprinted weights
Qi, H., Brown, M., and Lowe, D. G · 2018
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Meta-learning with latent embedding optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R · 2018
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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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A baseline for few-shot image classification
Dhillon, G. S., Chaudhari, P., Ravichandran, A., and Soatto, S · 2019
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Lvis: A dataset for large vocabulary instance segmentation
Gupta, A., Dollar, P., and Girshick, R · 2019
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Dynamic few-shot visual learning without forgetting
Gidaris, S. and Komodakis, N · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
Cited alongside, same era.
Tafe-net: Task-aware feature embeddings for low shot learning
Wang, X., Yu, F., Wang, R., Darrell, T., and Gonzalez, J. E
Cited in the paper.
Meta-learning to detect rare objects
Wang, Y.-X., Ramanan, D., and Hebert, M
Cited in the paper.
Few-shot object detection via feature reweighting
Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., and Darrell, T · 2019
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Meta r-cnn: Towards general solver for instance-level low-shot learning
Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., and Lin, L · 2019
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