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Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class.
Categorization and naming in children: Problems of induction
Ellen M. Markman · 1989
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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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Simultaneous detection and segmentation
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2014
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Daisy filter flow: A generalized discrete approach to dense correspondences
Hongsheng Yang, Wen-Yan Lin, and Jiangbo Lu · 2014
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The pascal visual object classes challenge: A retrospective
Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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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 Dollár · 2015
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Learning deconvolution network for semantic segmentation
Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han · 2015
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U-net: Convolutional networks for biomedical image segmentation
Thomas Brox Olaf Ronneberger, Philipp Fischer · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Proposal flow
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning multi-domain convolutional neural networks for visual tracking
Hyeonseob Nam and Bohyung Han · 2016
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Joint recovery of dense correspondence and cosegmentation in two images
Tatsunori Taniai, Sudipta N Sinha, and Yoichi Sato · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra · 2016
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Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors
Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk · 2017
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Densely connected convolutional networks
Gao Huang*, Zhuang Liu*, Laurens van der Maaten, and Kilian Weinberger · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Anchornet: A weakly supervised network to learn geometry-sensitive features for semantic matching
David Novotny, Diane Larlus, and Andrea Vedaldi · 2017
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Large kernel matters – improve semantic segmentation by global convolutional network
Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, and Jian Sun · 2017
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Convolutional neural network architecture for geometric matching
Ignacio Rocco, Relja Arandjelovic, and Josef Sivic · 2017
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One-shot learning for semantic segmentation
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots · 2017
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Fully convolutional networks for semantic segmentation
Evan Shelhamer, Jonathan Long, and Trevor Darrell · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Few-shot semantic segmentation with prototype learning
Nanqing Dong and Eric P. Xing · 2018
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Proposal flow: Semantic correspondences from object proposals
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
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Conditional networks for few-shot semantic segmentation
Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei Efros, and Sergey Levine · 2018
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Few-shot segmentation propagation with guided networks
Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei Efros, and Sergey Levine · 2018
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Neighbourhood consensus networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2018
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Few-shot learning with graph neural networks
Victor Garcia Satorras and Joan Bruna Estrach · 2018
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Benchmarking 6dof outdoor visual localization in changing conditions
Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenborg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, Fredrik Kahl, and Tomas Pajdla · 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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Simpropnet: Improved similarity propagation for few-shot image segmentation
Siddhartha Gairola, Mayur Hemani, Ayush Chopra, , and Balaji Krishnamurthy · 2020
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Robust image matching by dynamic feature selection
Hao Huang, Jianchun Chen, Xiang Li, Lingjing Wang, and Yi Fang · 2020
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Sideinfnet: A deep neural network for semi-automatic semantic segmentation with side information
Jing Yu Koh, Duc Thanh Nguyen, Quang-Trung Truong, Sai-Kit Yeung, and Alexander Binder · 2020
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Correspondence networks with adaptive neighbourhood consensus
Shuda Li, Kai Han, Theo W. Costain, Henry Howard-Jenkins, and Victor Prisacariu · 2020
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Dual-resolution correspondence networks
Xinghui Li, Kai Han, Shuda Li, and Victor Prisacariu · 2020
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Fss-1000: A 1000-class dataset for few-shot segmentation
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Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Infinite mixture prototypes for few-shot learning
Kelsey Allen, Evan Shelhamer, Hanul Shin, and Joshua Tenenbaum · 2019
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Cross attention network for few-shot classification
Ruibing Hou, Hong Chang, MA Bingpeng, Shiguang Shan, and Xilin Chen · 2019
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Attention-based multi-context guiding for few-shot semantic segmentation
Tao Hu, Pengwan Yang, Chiliang Zhang, Gang Yu, Yadong Mu, and Cees G. M. Snoek · 2019
Cited alongside, same era.
Dynamic context correspondence network for semantic alignment
Shuaiyi Huang, Qiuyue Wang, Songyang Zhang, Shipeng Yan, and Xuming He · 2019
Cited alongside, same era.
Sfnet: Learning object-aware semantic correspondence
Junghyup Lee, Dohyung Kim, Jean Ponce, and Bumsub Ham · 2019
Cited alongside, same era.
Fast online object tracking and segmentation: A unifying approach
Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, and Junjie Yan · 2019
Cited alongside, same era.
Xiang Li, Tianhan Wei, Yau Pun Chen, Yu-Wing Tai, and Chi-Keung Tang · 2020
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Crnet: Cross-reference networks for few-shot segmentation
Weide Liu, Chi Zhang, Guosheng Lin, and Fayao Liu · 2020
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Part-aware prototype network for few-shot semantic segmentation
Yongfei Liu, Xiangyi Zhang, Songyang Zhang, and Xuming He · 2020
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Semantic correspondence as an optimal transport problem
Yanbin Liu, Linchao Zhu, Makoto Yamada, and Yi Yang · 2020
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Semi-supervised semantic segmentation via strong-weak dual-branch network
Wenfeng Luo and Meng Yang · 2020
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Learning to compose hypercolumns for visual correspondence
Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho · 2020
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Efficient neighbourhood consensus networks via submanifold sparse convolutions
Ignacio Rocco, Relja Arandjelović, and Josef Sivic · 2020
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Mining cross-image semantics for weakly supervised semantic segmentation
Guolei Sun, Wenguan Wang, Jifeng Dai, and Luc Van Gool · 2020
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Differentiable meta-learning model for few-shot semantic segmentation
Pinzhuo Tian, Zhangkai Wu, Lei Qi, Lei Wang, Yinghuan Shi, and Yang Gao · 2020
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Prior guided feature enrichment network for few-shot segmentation
Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia · 2020
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GLU-Net: Global-local universal network for dense flow and correspondences
Prune Truong, Martin Danelljan, and Radu Timofte · 2020
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Regularized loss for weakly supervised single class semantic segmentation
Olga Veksler · 2020
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Few-shot semantic segmentation with democratic attention networks
Haochen Wang, Xudong Zhang, Yutao Hu, Yandan Yang, Xianbin Cao, and Xiantong Zhen · 2020
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Dual super-resolution learning for semantic segmentation
Li Wang, Dong Li, Yousong Zhu, Lu Tian, and Yi Shan · 2020
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Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation
Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen · 2020
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Prototype mixture models for few-shot semantic segmentation
Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Ye Qixiang · 2020
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A new local transformation module for few-shot segmentation
Yuwei Yang, Fanman Meng, Hongliang Li, Qingbo Wu, Xiaolong Xu, and Shuai Chen · 2020
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Few-shot learning via embedding adaptation with set-to-set functions
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2020
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Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers
Chi Zhang, Yujun Cai, Guosheng Lin, and Chunhua Shen · 2020
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Splitting vs. merging: Mining object regions with discrepancy and intersection loss for weakly supervised semantic segmentation
Tianyi Zhang, Guosheng Lin, Weide Liu, Jianfei Cai, and Alex Kot · 2020
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Sg-one: Similarity guidance network for one-shot semantic segmentation
Xiaolin Zhang, Yunchao Wei, Yi Yang, and Thomas S Huang · 2020
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On the texture bias for few-shot cnn segmentation
Reza Azad, Abdur R Fayjie, Claude Kauffman, Ismail Ben Ayed, Marco Pedersoli1, and Jose Dolz1 · 2021
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Few-shot segmentation without meta-learning: A good transductive inference is all you need?
Malik Boudiaf, Hoel Kervadec, Ziko Imtiaz Masud, Pablo Piantanida, Ismail Ben Ayed, and Jose Dolz · 2021
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Convolutional hough matching networks
Juhong Min and Minsu Cho · 2021
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