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We present a simple and practical framework for anomaly segmentation called Maskomaly.
Identification of outliers , volume 11
Douglas M Hawkins · 1980
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One versus all for deep neural network incertitude (ovnni) quantification
Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Séverine Dubuisson, and Isabelle Bloch · 2006
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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The cityscapes dataset
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Scharwächter, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 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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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 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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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 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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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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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, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2018
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Simultaneous semantic segmentation and outlier detection in presence of domain shift
Petra Bevandić, Ivan Krešo, Marin Oršić, and Siniša Šegvić · 2019
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Detecting the unexpected via image resynthesis
Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann · 2019
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
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Improving semantic segmentation via video propagation and label relaxation
Yi Zhu, Karan Sapra, Fitsum A Reda, Kevin J Shih, Shawn Newsam, Andrew Tao, and Bryan Catanzaro · 2019
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Tradi: Tracking deep neural network weight distributions
Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Séverine Dubuisson, and Isabelle Bloch · 2020
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Dense open-set recognition with synthetic outliers generated by real nvp
Matej Grcić, Petra Bevandić, and Siniša Šegvić · 2020
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Dense anomaly detection by robust learning on synthetic negative data
Matej Grcić, Petra Bevandić, and Siniša Šegvić · 2021
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Towards corner case detection by modeling the uncertainty of instance segmentation networks
Florian Heidecker, Abdul Hannan, Maarten Bieshaar, and Bernhard Sick · 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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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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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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Ivan Krešo, Josip Krapac, and Siniša Šegvić · 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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Detection and retrieval of out-of-distribution objects in semantic segmentation
Philipp Oberdiek, Matthias Rottmann, and Gernot A Fink · 2020
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Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F Núñez, and Jordi Luque · 2020
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Synthesize then compare: Detecting failures and anomalies for semantic segmentation
Yingda Xia, Wei Shen, et al · 2020
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Triggering failures: Out-of-distribution detection by learning from local adversarial attacks in semantic segmentation
Victor Besnier, Andrei Bursuc, David Picard, and Alexandre Briot · 2021
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Dense outlier detection and open-set recognition based on training with noisy negative images
Petra Bevandić, Ivan Krešo, Marin Oršić, and Siniša Šegvić · 2021
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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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Understanding failures in out-of-distribution detection with deep generative models
Lily Zhang, Mark Goldstein, and Rajesh Ranganath · 2021
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Masked-attention mask transformer for universal image segmentation
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar · 2022
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Is out-of-distribution detection learnable?
Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, and Feng Liu · 2022
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Densehybrid: Hybrid anomaly detection for dense open-set recognition
Matej Grcić, Petra Bevandić, and Siniša Šegvić · 2022
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Scaling out-of-distribution detection for real-world settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joe Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song · 2022
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Gmmseg: Gaussian mixture based generative semantic segmentation models
Chen Liang, Wenguan Wang, Jiaxu Miao, and Yi Yang · 2022
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Pixels together strong: Segmenting unknown regions rejected by all
Nazir Nayal, Mısra Yavuz, João F Henriques, and Fatma Güney · 2022
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Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes
Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro · 2022
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Hybrid open-set segmentation with synthetic negative data
Matej Grcić and Siniša Šegvić · 2023
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On advantages of mask-level recognition for open-set segmentation in the wild
Matej Grcić, Josip Šarić, and Siniša Šegvić · 2023
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Unmasking anomalies in road-scene segmentation
Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel, Carlo Masone, and Barbara Caputo · 2023
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