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In the past several years, road anomaly segmentation is actively explored in the academia and drawing growing attention in the industry.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Real-time small obstacle detection on highways using compressive RBM road reconstruction
Clement Creusot and Asim Munawar · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Virtual worlds as proxy for multi-object tracking analysis
Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
Earlier work this paper cites.
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
Earlier work this paper cites.
CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Earlier work this paper cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Playing for benchmarks
Stephan R Richter, Zeeshan Hayder, and Vladlen Koltun · 2017
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving
Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena · 2019
Cited alongside, same era.
nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
Cited alongside, same era.
Detecting the unexpected via image resynthesis
Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann · 2019
Cited alongside, same era.
Mseg: A composite dataset for multi-domain semantic segmentation
John Lambert, Zhuang Liu, Ozan Sener, James Hays, and Vladlen Koltun · 2020
Cited alongside, same era.
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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Vision transformer adapter for dense predictions
Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao · 2022
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Gianni Franchi, Xuanlong Yu, Andrei Bursuc, Rémi Kazmierczak, Séverine Dubuisson, Emanuel Aldea, and David Filliat · 2022
Later among the works it cites.
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, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song · 2022
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Towards streaming perception
Mengtian Li, Yu-Xiong Wang, and Deva Ramanan · 2020
Cited alongside, same era.
Detection and retrieval of out-of-distribution objects in semantic segmentation
Philipp Oberdiek, Matthias Rottmann, and Gernot A. Fink · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
Yingda Xia and Wei Shen · 2020
Cited alongside, same era.
Segmentmeifyoucan: A benchmark for anomaly segmentation
Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, and Matthias Rottmann · 2021
Cited alongside, same era.
Deep Anomaly Detection with Outlier Exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich
Cited in the paper.
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin
Cited in the paper.
Later among the works it cites.
Gmmseg: Gaussian mixture based generative semantic segmentation models
Chen Liang, Wenguan Wang, Jiaxu Miao, and Yi Yang · 2022
Later among the works it cites.
Residual pattern learning for pixel-wise out-of-distribution detection in semantic segmentation
Yuyuan Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro · 2022
Later among the works it cites.
Enhancing photorealism enhancement
Stephan R Richter, Hassan Abu AlHaija, and Vladlen Koltun · 2022
Later among the works it cites.
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
Later among the works it cites.
Eva: Exploring the limits of masked visual representation learning at scale
Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, and Yue Cao · 2023
Later among the works it cites.