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Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples.
A large-scale hierarchical image database
Jia Deng · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla SegNet · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Learning to learn: Model regression networks for easy small sample learning
Yu-Xiong Wang and Martial Hebert · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Proposal flow: Semantic correspondences from object proposals
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid · 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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One-shot learning for semantic segmentation
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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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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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
Cited alongside, same era.
Yolact: Real-time instance segmentation
Daniel Bolya, Chong Zhou, Fanyi Xiao, and Yong Jae Lee · 2019
Cited alongside, same era.
Image block augmentation for one-shot learning
Zitian Chen, Yanwei Fu, Kaiyu Chen, and Yu-Gang Jiang · 2019
Cited alongside, same era.
Image deformation meta-networks for one-shot learning
Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, and Martial Hebert · 2019
Cited alongside, same era.
Attention-based multi-context guiding for few-shot semantic segmentation
Tao Hu, Pengwan Yang, Chiliang Zhang, Gang Yu, Yadong Mu, and Cees GM Snoek · 2019
Cited alongside, same era.
Ccnet: Criss-cross attention for semantic segmentation
Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu · 2019
Learning to compose hypercolumns for visual correspondence
Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho · 2020
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Bidirectional pyramid networks for semantic segmentation
Dong Nie, Jia Xue, and Xiaofeng Ren · 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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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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Bi-directional attention for joint instance and semantic segmentation in point clouds
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Cited alongside, same era.
Task agnostic meta-learning for few-shot learning
Muhammad Abdullah Jamal and Guo-Jun Qi · 2019
Cited alongside, same era.
Finding task-relevant features for few-shot learning by category traversal
Hongyang Li, David Eigen, Samuel Dodge, Matthew Zeiler, and Xiaogang Wang · 2019
Cited alongside, same era.
Hyperpixel flow: Semantic correspondence with multi-layer neural features
Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho · 2019
Cited alongside, same era.
Feature weighting and boosting for few-shot segmentation
Khoi Nguyen and Sinisa Todorovic · 2019
Cited alongside, same era.
Adaptive masked proxies for few-shot segmentation
Mennatullah Siam, Boris Oreshkin, and Martin Jagersand · 2019
Cited alongside, same era.
Panet: Few-shot image semantic segmentation with prototype alignment
Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng · 2019
Cited alongside, same era.
Guangnan Wu, Zhiyi Pan, Peng Jiang, and Changhe Tu · 2020
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Polarmask: Single shot instance segmentation with polar representation
Enze Xie, Peize Sun, Xiaoge Song, Wenhai Wang, Xuebo Liu, Ding Liang, Chunhua Shen, and Ping Luo · 2020
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Prototype mixture models for few-shot semantic segmentation
Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye · 2020
Later among the works it cites.
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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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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Task-wise attention guided part complementary learning for few-shot image classification
Gong Cheng, Ruimin Li, Chunbo Lang, and Junwei Han · 2021
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Cost aggregation is all you need for few-shot segmentation
Sunghwan Hong, Seokju Cho, Jisu Nam, and Seungryong Kim · 2021
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Adaptive prototype learning and allocation for few-shot segmentation
Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, and Joongkyu Kim · 2021
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Anti-aliasing semantic reconstruction for few-shot semantic segmentation
Binghao Liu, Yao Ding, Jianbin Jiao, Xiangyang Ji, and Qixiang Ye · 2021
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Harmonic feature activation for few-shot semantic segmentation
Binghao Liu, Jianbin Jiao, and Qixiang Ye · 2021
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Simpler is better: Few-shot semantic segmentation with classifier weight transformer
Zhihe Lu, Sen He, Xiatian Zhu, Li Zhang, Yi-Zhe Song, and Tao Xiang · 2021
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Hypercorrelation squeeze for few-shot segmentation
Juhong Min, Dahyun Kang, and Minsu Cho · 2021
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Learning meta-class memory for few-shot semantic segmentation
Zhonghua Wu, Xiangxi Shi, Guosheng Lin, and Jianfei Cai · 2021
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Scale-aware graph neural network for few-shot semantic segmentation
Guo-Sen Xie, Jie Liu, Huan Xiong, and Ling Shao · 2021
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Self-guided and cross-guided learning for few-shot segmentation
Bingfeng Zhang, Jimin Xiao, and Terry Qin · 2021
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Few-shot segmentation via cycle-consistent transformer
Gengwei Zhang, Guoliang Kang, Yi Yang, and Yunchao Wei · 2021
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Learning what not to segment: A new perspective on few-shot segmentation
Chunbo Lang, Gong Cheng, Binfei Tu, and Junwei Han · 2022
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