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Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems.
Learning to Recognize Objects
Linda B Smith · 2003
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Caltech-UCSD birds 200
P. Welinder, S. Branson, T. Mita, and C. Wah · 2010
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Transfer Learning by Borrowing Examples for Multiclass Object Detection
J. J. Lim, R. Salakhutdinov, and A. Torralba · 2012
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Devise: A deep visual-semantic embedding model
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov · 2013
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Glove: Global Vectors for Word Representation
J. Pennington, R. Socher, and C. Manning · 2014
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Predicting deep zero-shot convolutional neural networks using textual descriptions
J. L. Ba, K. Swersky, S. Fidler, and R. Salakhutdinov · 2015
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Transductive Multi-View Zero-Shot Learning
Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong · 2015
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Articulated pose estimation with tiny synthetic videos
D. Park and D. Ramanan · 2015
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Metric Learning with Adaptive Density Discrimination
O. Rippel, M. Paluri, P. Dollar, and L. Bourdev · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Zero-shot learning via semantic similarity embedding
Z. Zhang and V. Saligrama · 2015
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Label-Embedding for Image Classification
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid · 2016
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Meta-Learning with Memory-Augmented Neural Networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap · 2016
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Matching Networks for One Shot Learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
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Learning to Learn: Model Regression Networks for Easy Small Sample Learning
Y.-X. Wang and M. Hebert · 2016
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Few-Shot Learning with Graph Neural Networks
V. Garcia and J. Bruna · 2017
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A Closer Look At Few-Shot Classification
W.-Y. Chen · 2018
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Semantic Feature Augmentation in Few-shot Learning
Z. Chen, Y. Fu, Y. Zhang, Y.-G. Jiang, X. Xue, and L. Sigal · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Learning to learn with conditional class dependencies
X. Jiang, M. Havaei, F. Varno, G. Chartrand, N. Chapados, and S. Matwin · 2018
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Meta-Learning with Latent Embedding Optimization
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell · 2018
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K. Guu, T. B. Hashimoto, Y. Oren, and P. Liang · 2017
Cited alongside, same era.
Low-shot Visual Recognition by Shrinking and Hallucinating Features
B. Hariharan and R. Girshick · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
Z. Li, F. Zhou, F. Chen, and H. Li · 2017
Cited alongside, same era.
Optimization As a Model for Few-Shot Learning
S. Ravi and H. Larochelle · 2017
Cited alongside, same era.
Prototypical Networks for Few-shot Learning
J. Snell, K. Swersky, and R. Zemel · 2017
Cited alongside, same era.
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images
A. Yu and K. Grauman · 2017
Cited alongside, same era.
Delta-Encoder: an Effective Sample Synthesis Method for Few-Shot Object Recognition
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. M. Bronstein · 2018
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Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales · 2018
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Attngan: Fine-grained text to image generation with attentional generative adversarial networks
T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, and X. He · 2018
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Deep Meta-Learning: Learning to Learn in the Concept Space
F. Zhou, B. Wu, and Z. Li · 2018
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Metadapt: Meta-learned task-adaptive architecture for few-shot classification
S. Doveh, E. Schwartz, C. Xue, R. Feris, A. Bronstein, R. Giryes, and L. Karlinsky · 2019
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Diversity with Cooperation: Ensemble Methods for Few-Shot Classification
N. Dvornik, C. Schmid, and J. Mairal · 2019
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Meta-Learning with Differentiable Convex Optimization
K. Lee, S. Maji, A. Ravichandran, S. Soatto, W. Services, U. C. San Diego, and U. Amherst · 2019
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Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Y. Wang, W.-L. Chao, K. Q. Weinberger, and L. van der Maaten · 2019
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Adaptive cross-modal few-shot learning
C. Xing, N. Rostamzadeh, B. Oreshkin, and P. O. Pinheiro · 2019
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