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Cross-domain object detection is more challenging than object classification since multiple objects exist in an image and the location of each object is unknown in the unlabeled target domain.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
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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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, and Qiang Yang · 2010
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
Domain adaptation for structured regression
M. Yamada, L. Sigal, and Yi Chang · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Fast r-cnn
Ross Girshick · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 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.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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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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Cross-domain weakly-supervised object detection through progressive domain adaptation
Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2020
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Tide: A general toolbox for identifying object detection errors
Daniel Bolya, Sean Foley, James Hays, and Judy Hoffman · 2020
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Harmonizing transferability and discriminability for adapting object detectors
Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou · 2020
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Domain adaptive object detection via asymmetric tri-way faster-rcnn
Zhenwei He and Lei Zhang · 2020
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Every pixel matters: Center-aware feature alignment for domain adaptive object detector
Cheng-Chun Hsu, Yi-Hsuan Tsai, Yen-Yu Lin, and Ming-Hsuan Yang · 2020
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Deep learning for generic object detection: A survey
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Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan · 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 mutual learning
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu · 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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Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang · 2019
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Note-rcnn: Noise tolerant ensemble rcnn for semi-supervised object detection
Jiyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, and Ram Nevatia · 2019
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Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen · 2020
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Domain adaptation for regression under beer–lambert’s law
Ramin Nikzad-Langerodi, Werner Zellinger, Susanne Saminger-Platz, and Bernhard A. Moser · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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Adapting object detectors with conditional domain normalization
Peng Su, Kun Wang, Xingyu Zeng, Shixiang Tang, Dapeng Chen, Di Qiu, and Xiaogang Wang · 2020
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Exploring categorical regularization for domain adaptive object detection
Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, and Xiu-Shen Wei · 2020
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Adaptive object detection with dual multi-label prediction
Zhen Zhao, Yuhong Guo, Haifeng Shen, and Jieping Ye · 2020
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Cross-domain object detection through coarse-to-fine feature adaptation
Yangtao Zheng, Di Huang, Songtao Liu, and Yunhong Wang · 2020
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Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning
Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, and Vicente Ordonez · 2021
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Representation subspace distance for domain adaptation regression
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Unbiased mean teacher for cross-domain object detection
Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan · 2021
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Regressive domain adaptation for unsupervised keypoint detection
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Unit: Unified knowledge transfer for any-shot object detection and segmentation
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Unbiased teacher for semi-supervised object detection
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Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection
Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A Sindagi, and Vishal M Patel · 2021
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