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Few-shot segmentation (FSS) aims at performing semantic segmentation on novel classes given a few annotated support samples.
Voronoi diagrams—a survey of a fundamental geometric data structure
Franz Aurenhammer · 1991
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
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Earlier work this paper cites.
The pascal visual object classes challenge 2012 (voc2012) development kit
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Few-shot semantic segmentation with prototype learning
Nanqing Dong and Eric Xing · 2018
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Conditional networks for few-shot semantic segmentation
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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
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Feature weighting and boosting for few-shot segmentation
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AMP: Adaptive masked proxies for few-shot segmentation
Mennatullah Siam, Boris N. Oreshkin, and Martin Jagersand · 2019
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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 · 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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A New Local Transformation Module for Few-shot Segmentation
Yuwei Yang, Fanman Meng, Hongliang Li, Qingbo Wu, Xiaolong Xu, and Shuai Chen · 2019
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Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation
Chi Zhang, Guosheng Lin, Fayao Liu, Jiushuang Guo, Qingyao Wu, and Rui Yao · 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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Language Models are Few-Shot Learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Language Models are Few-Shot Learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2021
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Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Simpler Is Better: Few-Shot Semantic Segmentation With Classifier Weight Transformer
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Hypercorrelation Squeeze for Few-Shot Segmentation
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SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentation
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Data-Efficient Image Recognition with Contrastive Predictive Coding
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FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
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CRNet: Cross-Reference Networks for Few-Shot Segmentation
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Part-Aware Prototype Network for Few-Shot Semantic Segmentation
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SML: Semantic Meta-learning for Few-shot Semantic Segmentation
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Prior Guided Feature Enrichment Network for Few-Shot Segmentation
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Training data-efficient image transformers & distillation through attention
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SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
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Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation
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Few-Shot Semantic Segmentation With Cyclic Memory Network
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Mining Latent Classes for Few-Shot Segmentation
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Associating Objects with Transformers for Video Object Segmentation
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Self-Guided and Cross-Guided Learning for Few-Shot Segmentation
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Few-shot segmentation via cycle-consistent transformer
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Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes
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Domain Adaptation via Prompt Learning
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Switch to generalize: Domain-switch learning for cross-domain few-shot classification
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Learning What Not to Segment: A New Perspective on Few-Shot Segmentation
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