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Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regimes.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L.; Fergus, R.; and Perona, P. 2004 · 2004
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Automated flower classification over a large number of classes
Nilsback, M.-E.; and Zisserman, A. 2008 · 2008
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Extracting and Composing Robust Features with Denoising Autoencoders
Vincent, P.; Larochelle, H.; Bengio, Y.; and Manzagol, P.-A. 2008 · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Conditional Prompt Learning for Vision-Language Models
Zhou, K.; Yang, J.; Loy, C. C.; and Liu, Z. 2022 · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J.; Hays, J.; Ehinger, K. A.; Oliva, A.; and Torralba, A. 2010 · 2010
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Cats and dogs
Parkhi, O. M.; Vedaldi, A.; Zisserman, A.; and Jawahar, C. 2012 · 2012
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UCF101: A dataset of 101 human actions classes from videos in the wild
Soomro, K.; Zamir, A. R.; and Shah, M. 2012 · 2012
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3d object representations for fine-grained categorization
Krause, J.; Stark, M.; Deng, J.; and Fei-Fei, L. 2013 · 2013
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Fine-grained visual classification of aircraft
Maji, S.; Rahtu, E.; Kannala, J.; Blaschko, M.; and Vedaldi, A. 2013 · 2013
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Food-101–mining discriminative components with random forests
Bossard, L.; Guillaumin, M.; and Gool, L. V. 2014 · 2014
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Describing textures in the wild
Cimpoi, M.; Maji, S.; Kokkinos, I.; Mohamed, S.; and Vedaldi, A. 2014 · 2014
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VQA: Visual Question Answering
Antol, S.; Agrawal, A.; Lu, J.; Mitchell, M.; Batra, D.; Zitnick, C. L.; and Parikh, D. 2015 · 2015
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Unsupervised Visual Representation Learning by Context Prediction
Doersch, C.; Gupta, A.; and Efros, A. A. 2015 · 2015
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How smooth are particle trajectories in a CDM Universe?
Rampf, C.; Villone, B.; and Frisch, U. 2015 · 2015
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Noroozi, M.; and Favaro, P. 2016 · 2016
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Context Encoders: Feature Learning by Inpainting
Pathak, D.; Krahenbuhl, P.; Donahue, J.; Darrell, T.; and Efros, A. A. 2016 · 2016
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Matching Networks for One Shot Learning
Vinyals, O.; Blundell, C.; Lillicrap, T.; kavukcuoglu, k.; and Wierstra, D. 2016 · 2016
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Learning to Learn: Model Regression Networks for Easy Small Sample Learning
Wang, Y.; and Hebert, M. 2016 · 2016
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Low-Shot Visual Recognition by Shrinking and Hallucinating Features
Hariharan, B.; and Girshick, R. B. 2017 · 2017
Cited alongside, same era.
Focal Loss for Dense Object Detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollar, P. 2017 · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
Cited alongside, same era.
Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L. u.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Deep Clustering for Unsupervised Learning of Visual Features
Caron, M.; Bojanowski, P.; Joulin, A.; and Douze, M. 2018 · 2018
Cited alongside, same era.
Momentum Contrast for Unsupervised Visual Representation Learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers
Zhang, C.; Cai, Y.; Lin, G.; and Shen, C. 2020 · 2020
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Emerging Properties in Self-Supervised Vision Transformers
Caron, M.; Touvron, H.; Misra, I.; Jégou, H.; Mairal, J.; Bojanowski, P.; and Joulin, A. 2021 · 2021
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Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Chen, Y.; Liu, Z.; Xu, H.; Darrell, T.; and Wang, X. 2021 · 2021
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DALL·E Mini
Dayma, B.; Patil, S.; Cuenca, P.; Saifullah, K.; Abraham, T.; Le Khac, P.; Melas, L.; and Ghosh, R. 2021 · 2021
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CogView: Mastering Text-to-Image Generation via Transformers
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2018 · 2018
Cited alongside, same era.
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
Sharma, P.; Ding, N.; Goodman, S.; and Soricut, R. 2018 · 2018
Cited alongside, same era.
Learning to Compare: Relation Network for Few-Shot Learning
Sung, F.; Yang, Y.; Zhang, L.; Xiang, T.; Torr, P. H.; and Hospedales, T. M. 2018 · 2018
Cited alongside, same era.
Representation Learning with Contrastive Predictive Coding
van den Oord, A.; Li, Y.; and Vinyals, O. 2018 · 2018
Cited alongside, same era.
Rethinking ImageNet Pre-Training
He, K.; Girshick, R.; and Dollar, P. 2019 · 2019
Cited alongside, same era.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Helber, P.; Bischke, B.; Dengel, A.; and Borth, D. 2019 · 2019
Cited alongside, same era.
Ding, M.; Yang, Z.; Hong, W.; Zheng, W.; Zhou, C.; Yin, D.; Lin, J.; Zou, X.; Shao, Z.; Yang, H.; and Tang, J. 2021 · 2021
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CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Gao, P.; Geng, S.; Zhang, R.; Ma, T.; Fang, R.; Zhang, Y.; Li, H.; and Qiao, Y. 2021 · 2021
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Natural adversarial examples
Hendrycks, D.; Zhao, K.; Basart, S.; Steinhardt, J.; and Song, D. 2021 · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C.; Yang, Y.; Xia, Y.; Chen, Y.-T.; Parekh, Z.; Pham, H.; Le, Q.; Sung, Y.-H.; Li, Z.; and Duerig, T. 2021 · 2021
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Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L.; and Tian, Y. 2021 · 2021
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Beyond max-margin: Class margin equilibrium for few-shot object detection
Li, B.; Yang, B.; Liu, C.; Liu, F.; Ji, R.; and Ye, Q. 2021 · 2021
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Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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Zero-Shot Text-to-Image Generation
Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021 · 2021
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Free lunch for few-shot learning: Distribution calibration
Yang, S.; Liu, L.; and Xu, M. 2021 · 2021
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Tip-adapter: Training-free clip-adapter for better vision-language modeling
Zhang, R.; Fang, R.; Gao, P.; Zhang, W.; Li, K.; Dai, J.; Qiao, Y.; and Li, H. 2021 · 2021
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Learning to Prompt for Vision-Language Models
Zhou, K.; Yang, J.; Loy, C. C.; and Liu, Z. 2021 · 2021
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Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding
Afham, M.; Dissanayake, I.; Dissanayake, D.; Dharmasiri, A.; Thilakarathna, K.; and Rodrigo, R. 2022 · 2022
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Masked Autoencoders Are Scalable Vision Learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
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