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Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much.
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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Pedestrian detection: An evaluation of the state of the art
Piotr Dollar, Christian Wojek, Bernt Schiele, and Pietro Perona · 2012
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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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Best of both worlds: human-machine collaboration for object annotation
Olga Russakovsky, Li-Jia Li, and Li Fei-Fei · 2015
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What’s the point: Semantic segmentation with point supervision
Amy Bearman, Olga Russakovsky, Vittorio Ferrari, and Li Fei-Fei · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 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.
Large scale semi-supervised object detection using visual and semantic knowledge transfer
Yuxing Tang, Josiah Wang, Boyang Gao, Emmanuel Dellandréa, Robert Gaizauskas, and Liming Chen · 2016
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
Terrance Devries and Graham W. Taylor · 2017
Earlier work this paper cites.
Deep self-taught learning for weakly supervised object localization
Zequn Jie, Yunchao Wei, Xiaojie Jin, Jiashi Feng, and Wei Liu · 2017
Earlier work this paper cites.
Weakly supervised object localization using things and stuff transfer
Miaojing Shi, Holger Caesar, and Vittorio Ferrari · 2017
Earlier work this paper cites.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Earlier work this paper cites.
Weakly-and semi-supervised object detection with expectation-maximization algorithm
Ziang Yan, Jian Liang, Weishen Pan, Jin Li, and Changshui Zhang · 2017
Cited alongside, same era.
Soft proposal networks for weakly supervised object localization
Yi Zhu, Yanzhao Zhou, Qixiang Ye, Qiang Qiu, and Jianbin Jiao · 2017
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Shin Ishii, and Masanori Koyama · 2018
Cited alongside, same era.
Between-class learning for image classification
Yuji Tokozume, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Cited alongside, same era.
Collaborative learning for weakly supervised object detection
Jiajie Wang, Jiangchao Yao, Ya Zhang, and Rui Zhang · 2018
Cited alongside, same era.
Consistency-based semi-supervised learning for object detection
Jisoo Jeong, Seungeui Lee, Jeesoo Kim, and Nojun Kwak · 2019
Later among the works it cites.
Daesik Kim, Gyujeong Lee, Jisoo Jeong, and Nojun Kwak · 2019
Later among the works it cites.
Semi-supervised object detection with unlabeled data
Nhu-Van Nguyen, Christophe Rigaud, and Jean-Christophe Burie · 2019
Later among the works it cites.
Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
Later among the works it cites.
Interpolation consistency training for semi-supervised learning
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
Later among the works it cites.
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Towards human-machine cooperation: Self-supervised sample mining for object detection
Keze Wang, Xiaopeng Yan, Dongyu Zhang, Lei Zhang, and Liang Lin · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
On adversarial mixup resynthesis
Christopher Beckham, Sina Honari, Vikas Verma, Alex M Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, and Chris Pal · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
Cited alongside, same era.
Understanding and improving interpolation in autoencoders via an adversarial regularizer
David Berthelot, Colin Raffel, Aurko Roy, and Ian Goodfellow · 2019
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space, 2019
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2019
Cited alongside, same era.
Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, and Jian Tang · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Later among the works it cites.
Bag of freebies for training object detection neural networks
Zhi Zhang, Tong He, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2019
Later among the works it cites.
Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2020
Closest in time.
Mixup regularization for region proposal based object detectors
Shahine Bouabid and Vincent Delaitre · 2020
Closest in time.
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
Closest in time.