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Over the past few years, we have witnessed the success of deep learning in image recognition thanks to the availability of large-scale human-annotated datasets such as PASCAL VOC, ImageNet, and COCO.
"grabcut": interactive foreground extraction using iterated graph cuts
Carsten Rother, Vladimir Kolmogorov, and Andrew Blake · 2004
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One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
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ImageNet: A Large-Scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman · 2010
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Bag-of-visual-words and spatial extensions for land-use classification
Yi Yang and Shawn Newsam · 2010
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Deep neural networks segment neuronal membranes in electron microscopy images
Dan C. Ciresan, Alessandro Giusti, Luca Maria Gambardella, and Jürgen Schmidhuber · 2012
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Learning hierarchical features for scene labeling
Clément Farabet, Camille Couprie, Laurent Najman, and Yann LeCun · 2013
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Simultaneous detection and segmentation
Bharath Hariharan, Pablo Andrés Arbeláez, Ross B. Girshick, and Jitendra Malik · 2014
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollar · 2014
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Recurrent convolutional neural networks for scene labeling
Pedro H. O. Pinheiro and Ronan Collobert · 2014
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Sun database: Exploring a large collection of scene categories
Jianxiong Xiao, Krista A. Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2014
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
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Siamese neural networks for one-shot image recognition
Gregory R. Koch · 2015
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Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Philipp Fischer Olaf Ronneberger and Thomas Brox · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Counting in the wild
Carlos Arteta, Victor Lempitsky, and Andrew Zisserman · 2016
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Fully convolutional instance-aware semantic segmentation
Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, and Yichen Wei · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2017
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2017
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Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Cited alongside, same era.
Learning feed-forward one-shot learners
Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, and Andrea Vedaldi · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Instance-aware semantic segmentation via multi-task network cascades
Dai Jifeng, He Kaiming, and Sun Jian · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy P. Lillicrap · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy P. Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots · 2017
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Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, and Vittorio Ferrari · 2018
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Few-shot segmentation propagation with guided networks
Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, and Sergey Levine · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Panet: Few-shot image semantic segmentation with prototype alignment
Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng · 2019
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Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning
Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, and Chunhua Shen · 2019
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