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Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of p(class|pixel feature).
The efficiency of logistic regression compared to normal discriminant analysis
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Classification with hybrid generative/discriminative models
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The tradeoff between generative and discriminative classifiers
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Clustering on the unit hypersphere using von mises-fisher distributions
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Principled hybrids of generative and discriminative models
Julia A Lasserre, Christopher M Bishop, and Thomas P Minka · 2006
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Generative or discriminative? getting the best of both worlds
JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith, and M West · 2007
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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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A gaussian mixture model layer jointly optimized with discriminative features within a deep neural network architecture
Ehsan Variani, Erik McDermott, and Georg Heigold · 2015
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Integrating gaussian mixtures into deep neural networks: Softmax layer with hidden variables
Zoltán Tüske, Muhammad Ali Tahir, Ralf Schlüter, and Hermann Ney · 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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Conditional random fields as recurrent neural networks
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr · 2015
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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 S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 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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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
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Large-margin softmax loss for convolutional neural networks
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Lost and found: detecting small road hazards for self-driving vehicles
Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, and Rudolf Mester · 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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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Deep learning markov random field for semantic segmentation
Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, and Xiaoou Tang · 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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Segmentation-aware convolutional networks using local attention masks
Adam W Harley, Konstantinos G Derpanis, and Iasonas Kokkinos · 2017
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Towards the first adversarially robust neural network model on mnist
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2018
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2018
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Robust detection of adversarial attacks by modeling the intrinsic properties of deep neural networks
Sinkhorn em: an expectation-maximization algorithm based on entropic optimal transport
Gonzalo Mena, Amin Nejatbakhsh, Erdem Varol, and Jonathan Niles-Weed · 2020
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Synthesize then compare: Detecting failures and anomalies for semantic segmentation
Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, and Alan L Yuille · 2020
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Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities
Matthias Rottmann, Pascal Colling, Thomas Paul Hack, Robin Chan, Fabian Hüger, Peter Schlicht, and Hanno Gottschalk · 2020
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Object-contextual representations for semantic segmentation
Yuhui Yuan, Xilin Chen, and Jingdong Wang · 2020
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Deep high-resolution representation learning for visual recognition
Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al · 2020
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Zhihao Zheng and Pengyu Hong · 2018
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2018
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
KIMIN LEE, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Cited alongside, same era.
Evaluating bayesian deep learning methods for semantic segmentation
Jishnu Mukhoti and Yarin Gal · 2018
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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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Unified perceptual parsing for scene understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun · 2018
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Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018
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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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Class-wise dynamic graph convolution for semantic segmentation
Hanzhe Hu, Deyi Ji, Weihao Gan, Shuai Bai, Wei Wu, and Junjie Yan · 2020
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Context prior for scene segmentation
Changqian Yu, Jingbo Wang, Changxin Gao, Gang Yu, Chunhua Shen, and Nong Sang · 2020
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Hierarchical human parsing with typed part-relation reasoning
Wenguan Wang, Hailong Zhu, Jifeng Dai, Yanwei Pang, Jianbing Shen, and Ling Shao · 2020
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Api-net: Robust generative classifier via a single discriminator
Xinshuai Dong, Hong Liu, Rongrong Ji, Liujuan Cao, Qixiang Ye, Jianzhuang Liu, and Qi Tian · 2020
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Understanding the limitations of conditional generative models
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, and Richard Zemel · 2020
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Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 2020
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Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi · 2020
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MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark
MMSegmentation Contributors · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
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Segmenter: Transformer for semantic segmentation
Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
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Hrformer: High-resolution transformer for dense prediction
Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, and Jingdong Wang · 2021
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A discriminative gaussian mixture model with sparsity
Hideaki Hayashi and Seiichi Uchida · 2021
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Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation
Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo · 2021
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Generative classifiers as a basis for trustworthy image classification
Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, and Carsten Rother · 2021
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Pixel-wise anomaly detection in complex driving scenes
Giancarlo Di Biase, Hermann Blum, Roland Siegwart, and Cesar Cadena · 2021
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Road anomaly detection by partial image reconstruction with segmentation coupling
Tomas Vojir, Tomáš Šipka, Rahaf Aljundi, Nikolay Chumerin, Daniel Olmeda Reino, and Jiri Matas · 2021
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Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation
Robin Chan, Matthias Rottmann, and Hanno Gottschalk · 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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The fishyscapes benchmark: Measuring blind spots in semantic segmentation
Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena · 2021
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Exploit visual dependency relations for semantic segmentation
Mingyuan Liu, Dan Schonfeld, and Wei Tang · 2021
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Specialize and fuse: Pyramidal output representation for semantic segmentation
Chi-Wei Hsiao, Cheng Sun, Hwann-Tzong Chen, and Min Sun · 2021
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Isnet: Integrate image-level and semantic-level context for semantic segmentation
Zhenchao Jin, Bin Liu, Qi Chu, and Nenghai Yu · 2021
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Mining contextual information beyond image for semantic segmentation
Zhenchao Jin, Tao Gong, Dongdong Yu, Qi Chu, Jian Wang, Changhu Wang, and Jie Shao · 2021
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Exploring cross-image pixel contrast for semantic segmentation
Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu, and Luc Van Gool · 2021
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Vspw: A large-scale dataset for video scene parsing in the wild
Jiaxu Miao, Yunchao Wei, Yu Wu, Chen Liang, Guangrui Li, and Yi Yang · 2021
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Collaborative video object segmentation by multi-scale foreground-background integration
Zongxin Yang, Yunchao Wei, and Yi Yang · 2021
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Associating objects with transformers for video object segmentation
Zongxin Yang, Yunchao Wei, and Yi Yang · 2021
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Per-pixel classification is not all you need for semantic segmentation
Bowen Cheng, Alexander G. Schwing, and Alexander Kirillov · 2021
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Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies
Yi Yang, Yueting Zhuang, and Yunhe Pan · 2021
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Rethinking semantic segmentation: A prototype view
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