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Cross-domain object detection has recently attracted more and more attention for real-world applications, since it helps build robust detectors adapting well to new environments.
Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Object detection with discriminatively trained part-based models
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan · 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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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Fast r-cnn
Ross Girshick · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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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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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
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?
Matthew Johnson-Roberson, Charles Barto, Rounak Mehta, Sharath Nittur Sridhar, Karl Rosaen, and Ram Vasudevan · 2016
Cited alongside, same era.
Cross-domain weakly-supervised object detection through progressive domain adaptation
Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Semantic foggy scene understanding with synthetic data
Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
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Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
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Exploring object relation in mean teacher for cross-domain detection
Qi Cai, Yingwei Pan, Chong-Wah Ngo, Xinmei Tian, Lingyu Duan, and Ting Yao · 2019
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Multi-adversarial faster-rcnn for unrestricted object detection
Zhenwei He and Lei Zhang · 2019
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
Cited alongside, same era.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
Domain adaptive faster r-cnn for object detection in the wild
Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
Cited alongside, same era.
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Diversify and match: A domain adaptive representation learning paradigm for object detection
Taekyung Kim, Minki Jeong, Seunghyeon Kim, Seokeon Choi, and Changick Kim · 2019
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Strong-weak distribution alignment for adaptive object detection
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko · 2019
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Pixel and feature level based domain adaptation for object detection in autonomous driving
Yuhu Shan, Wen Feng Lu, and Chee Meng Chew · 2019
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Wasserstein distance based domain adaptation for object detection
Pengcheng Xu, Prudhvi Gurram, Gene Whipps, and Rama Chellappa · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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Adapting object detectors via selective cross-domain alignment
Xinge Zhu, Jiangmiao Pang, Ceyuan Yang, Jianping Shi, and Dahua Lin · 2019
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