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Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation.
Weakly supervised learning of instance segmentation with inter-pixel relations
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
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Training products of experts by minimizing contrastive divergence
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Improved baselines with momentum contrastive learning
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RAFT: recurrent all-pairs field transforms for optical flow
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Big self-supervised models are strong semi-supervised learners
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Fast approximate spectral clustering
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Deep sparse rectifier neural networks
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Efficient inference in fully connected crfs with gaussian edge potentials
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Scikit-learn: Machine learning in python
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2014
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Adam: A method for stochastic optimization
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Learning visual groups from co-occurrences in space and time
Phillip Isola, Daniel Zoran, Dilip Krishnan, and Edward H Adelson · 2015
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Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
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Methods and datasets on semantic segmentation: A review
Hongshan Yu, Zhengeng Yang, Lei Tan, Yaonan Wang, Wei Sun, Mingui Sun, and Yandong Tang · 2018
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On the importance of label quality for semantic segmentation
Aleksandar Zlateski, Ronnachai Jaroensri, Prafull Sharma, and Frédo Durand · 2018
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Pytorch lightning
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, João F Henriques, and Andrea Vedaldi · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Fully convolutional networks for semantic segmentation
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Slic superpixels for efficient graph-based dimensionality reduction of hyperspectral imagery
Xuewen Zhang, Selene E Chew, Zhenlin Xu, and Nathan D Cahill · 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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Context encoders: Feature learning by inpainting
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Learning deep features for discriminative localization
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Rethinking atrous convolution for semantic image segmentation
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Mini-batch spectral clustering
Yufei Han and Maurizio Filippone · 2017
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graph2vec: Learning distributed representations of graphs
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Eccv 2020 tutorial on weakly-supervised learning in computer vision
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An image is worth 16x16 words: Transformers for image recognition at scale
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Leveraging instance-, image- and dataset-level information for weakly supervised instance segmentation
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Autoregressive unsupervised image segmentation
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Unsupervised learning of dense visual representations
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Ufo2: A unified framework towards omni-supervised object detection
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Scan: Learning to classify images without labels
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Emerging properties in self-supervised vision transformers
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Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering
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Model-agnostic explainability for visual search
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Unsupervised image segmentation by mutual information maximization and adversarial regularization
S Ehsan Mirsadeghi, Ali Royat, and Hamid Rezatofighi · 2021
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Weakly-supervised image semantic segmentation using graph convolutional networks
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Vision transformers for dense prediction
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Training data-efficient image transformers & distillation through attention
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Unsupervised semantic segmentation by contrasting object mask proposals
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