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Most progress in semantic segmentation reports on daytime images taken under favorable illumination conditions.
Vision and the atmosphere
S. G. Narasimhan and S. K. Nayar · 2002
Earlier work this paper cites.
Pedestrian detection and tracking with night vision
F. Xu, X. Liu, and K. Fujimura · 2005
Earlier work this paper cites.
Night-time pedestrian detection by visual-infrared video fusion
Y. Chen and C. Han · 2008
Earlier work this paper cites.
Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
Earlier work this paper cites.
Real-time pedestrian detection and tracking at nighttime for driver-assistance systems
J. Ge, Y. Luo, and G. Tei · 2009
Earlier work this paper cites.
A fast approximation of the bilateral filter using a signal processing approach
S. Paris and F. Durand · 2009
Earlier work this paper cites.
The PASCAL visual object classes (VOC) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Road detection based on illuminant invariance
J. M. A. Alvarez and A. M. Lopez · 2011
Earlier work this paper cites.
Continuous manifold based adaptation for evolving visual domains
J. Hoffman, T. Darrell, and K. Saenko · 2014
Earlier work this paper cites.
Unsupervised image transformation for outdoor semantic labelling
G. Ros and J. M. Alvarez · 2015
Earlier work this paper cites.
The Cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Earlier work this paper cites.
Looking at vehicles in the night: Detection and dynamics of rear lights
R. K. Satzoda and M. M. Trivedi · 2016
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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What uncertainties do we need in Bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
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Convolutional neural network-based human detection in nighttime images using visible light camera sensors
J. H. Kim, H. G. Hong, and K. R. Park · 2017
Earlier work this paper cites.
RefineNet: Multi-path refinement networks with identity mappings for high-resolution semantic segmentation
G. Lin, A. Milan, C. Shen, and I. Reid · 2017
Earlier work this paper cites.
1 year, 1000 km: The Oxford RobotCar dataset
W. Maddern, G. Pascoe, C. Linegar, and P. Newman · 2017
Cited alongside, same era.
The Mapillary Vistas dataset for semantic understanding of street scenes
G. Neuhold, T. Ollmann, S. Rota Bulò, and P. Kontschieder · 2017
Cited alongside, same era.
The Raincouver scene parsing benchmark for self-driving in adverse weather and at night
F. Tung, J. Chen, L. Meng, and J. J. Little · 2017
Cited alongside, same era.
AdapNet: Adaptive semantic segmentation in adverse environmental conditions
A. Valada, J. Vertens, A. Dhall, and W. Burgard · 2017
Cited alongside, same era.
Addressing appearance change in outdoor robotics with adversarial domain adaptation
M. Wulfmeier, A. Bewley, and I. Posner · 2017
Cited alongside, same era.
How good is my test data? Introducing safety analysis for computer vision
Semantic foggy scene understanding with synthetic data
C. Sakaridis, D. Dai, and L. Van Gool · 2018
Later among the works it cites.
Learning from synthetic data: Addressing domain shift for semantic segmentation
S. Sankaranarayanan, Y. Balaji, A. Jain, S. N. Lim, and R. Chellappa · 2018
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Benchmarking 6DOF outdoor visual localization in changing conditions
T. Sattler, W. Maddern, C. Toft, A. Torii, L. Hammarstrand, E. Stenborg, D. Safari, M. Okutomi, M. Pollefeys, J. Sivic, F. Kahl, and T. Pajdla · 2018
Later among the works it cites.
Learning to adapt structured output space for semantic segmentation
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker · 2018
Later among the works it cites.
DCAN: Dual channel-wise alignment networks for unsupervised scene adaptation
Z. Wu, X. Han, Y.-L. Lin, M. G. Uzunbas, T. Goldstein, S. Nam Lim, and L. S. Davis · 2018
Later among the works it cites.
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O. Zendel, M. Murschitz, M. Humenberger, and W. Herzner · 2017
Cited alongside, same era.
Curriculum domain adaptation for semantic segmentation of urban scenes
Y. Zhang, P. David, and B. Gong · 2017
Cited alongside, same era.
Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Cited alongside, same era.
Benchmarking image sensors under adverse weather conditions for autonomous driving
M. Bijelic, T. Gruber, and W. Ritter · 2018
Cited alongside, same era.
ROAD: Reality oriented adaptation for semantic segmentation of urban scenes
Y. Chen, W. Li, and L. Van Gool · 2018
Cited alongside, same era.
Dark model adaptation: Semantic image segmentation from daytime to nighttime
D. Dai and L. Van Gool · 2018
Cited alongside, same era.
M. Wulfmeier, A. Bewley, and I. Posner · 2018
Later among the works it cites.
BDD100K: a diverse driving video database with scalable annotation tooling
F. Yu, W. Xian, Y. Chen, F. Liu, M. Liao, V. Madhavan, and T. Darrell · 2018
Later among the works it cites.
WildDash - creating hazard-aware benchmarks
O. Zendel, K. Honauer, M. Murschitz, D. Steininger, and G. Fernandez Dominguez · 2018
Later among the works it cites.
Fully convolutional adaptation networks for semantic segmentation
Y. Zhang, Z. Qiu, T. Yao, D. Liu, and T. Mei · 2018
Later among the works it cites.
Penalizing top performers: Conservative loss for semantic segmentation adaptation
X. Zhu, H. Zhou, C. Yang, J. Shi, and D. Lin · 2018
Later among the works it cites.
Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Y. Zou, Z. Yu, B. Vijaya Kumar, and J. Wang · 2018
Later among the works it cites.
Curriculum model adaptation with synthetic and real data for semantic foggy scene understanding
D. Dai, C. Sakaridis, S. Hecker, and L. Van Gool · 2019
Closest in time.
Panoptic segmentation
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár · 2019
Closest in time.
A cross-season correspondence dataset for robust semantic segmentation
M. Larsson, E. Stenborg, L. Hammarstrand, M. Pollefeys, T. Sattler, and F. Kahl · 2019
Closest in time.
Bidirectional learning for domain adaptation of semantic segmentation
Y. Li, L. Yuan, and N. Vasconcelos · 2019
Closest in time.