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The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
Mmdetection: Open mmlab detection toolbox and benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al · 1906
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The Pascal Visual Object Classes (VOC) Challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman · 2010
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Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 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 Bernstein, Alexander C Berg, and Li Fei-Fei · 2015
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UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking
Longyin Wen, Dawei Du, Zhaowei Cai, Zhen Lei, Ming-Ching Chang, Honggang Qi, Jongwoo Lim, Ming-Hsuan Yang, and Siwei Lyu · 2015
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Simulating photo-realistic snow and fog on existing images for enhanced CNN training and evaluation
Dennis Hospach, Stefan Müller, Wolfgang Rosenstiel, and Oliver Bringmann · 2016
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Understanding how image quality affects deep neural networks
Samuel Fuller Dodge and Lina J. Karam · 2016
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Examining the impact of blur on recognition by convolutional networks
Igor Vasiljevic, Ayan Chakrabarti, and Gregory Shakhnarovich · 2016
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Virtual worlds as proxy for multi-object tracking analysis
Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig · 2016
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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
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Peter Radecki, Mark Campbell, and Kevin Matzen · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Playing for benchmarks
Stephan R. Richter, Zeeshan Hayder, and Vladlen Koltun · 2017
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Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?
M. Johnson-Roberson, Charles Barto, Rounak Mehta, Sharath Nittur Sridhar, Karl Rosaen, and Ram Vasudevan · 2017
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Towards practical verification of machine learning: The case of computer vision systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 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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The mapillary vistas dataset for semantic understanding of street scenes
Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulò, and Peter Kontschieder · 2017
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Openimages: A public dataset for large-scale multi-label and multi-class image classification
Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Shahab Kamali, Matteo Malloci, Jordi Pont-Tuset, Andreas Veit, Serge Belongie, Victor Gomes, Abhinav Gupta, Chen Sun, Gal Chechik, David Cai, Zheyun Feng, Dhyanesh Narayanan, and Kevin Murphy · 2017
Domain adaptive faster R-CNN for object detection in the wild
Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
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Development of a self-driving car that can handle the adverse weather
Unghui Lee, Jiwon Jung, Seokwoo Jung, and David Hyunchul Shim · 2018
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Rendering physically correct raindrops on windshields for robustness verification of camera-based object recognition
Alexander Von Bernuth, Georg Volk, and Oliver Bringmann · 2018
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Bdd100k: A diverse driving video database with scalable annotation tooling
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell · 2018
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An evaluation metric for object detection algorithms in autonomous navigation systems and its application to a real-time alerting system
Harshitha Machiraju and Sumohana Channappayya · 2018
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 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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Xun Huang and Serge Belongie · 2017
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos RM Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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“Death is certain, the time is not”: mortality and survival in Game of Thrones
Reidar P Lystad and Benjamin T Brown · 2018
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Cascade R-CNN: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Deformable convnets v2: More deformable, better results
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai · 2018
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Michael A Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, and Anh Nguyen · 2019
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Learning to remove rain in traffic surveillance by using synthetic data
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
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D2-city: A large-scale dashcam video dataset of diverse traffic scenarios
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nuscenes: A multimodal dataset for autonomous driving
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