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Recent advances in semi-supervised object detection (SSOD) are largely driven by consistency-based pseudo-labeling methods for image classification tasks, producing pseudo labels as supervisory signals.
Zhou, X.; Wang, D.; and Krähenbühl, P. 2019 · 1904
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Rapid object detection using a boosted cascade of simple features
Viola, P.; and Jones, M. 2001 · 2001
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A simple semi-supervised learning framework for object detection
Sohn, K.; Zhang, Z.; Li, C.-L.; Zhang, H.; Lee, C.-Y.; and Pfister, T. 2020b · 2005
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Object detection with discriminatively trained part-based models
Felzenszwalb, P. F.; Girshick, R. B.; McAllester, D.; and Ramanan, D. 2009 · 2009
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The pascal visual object classes (voc) challenge
Everingham, M.; Van Gool, L.; Williams, C. K.; Winn, J.; and Zisserman, A. 2010 · 2010
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Learning with pseudo-ensembles
Bachman, P.; Alsharif, O.; and Precup, D. 2014 · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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The cityscapes dataset for semantic urban scene understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Ssd: Single shot multibox detector
Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.-Y.; and Berg, A. C. 2016 · 2016
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You only look once: Unified, real-time object detection
Redmon, J.; Divvala, S.; Girshick, R.; and Farhadi, A. 2016 · 2016
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Improved regularization of convolutional neural networks with cutout
DeVries, T.; and Taylor, G. W. 2017 · 2017
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Temporal ensembling for semi-supervised learning
Laine, S.; and Aila, T. 2017 · 2017
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Feature pyramid networks for object detection
Lin, T.-Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; and Belongie, S. 2017 · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A.; and Valpola, H. 2017 · 2017
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Han, B.; Yao, Q.; Yu, X.; Niu, G.; Xu, M.; Hu, W.; Tsang, I.; and Sugiyama, M. 2018 · 2018
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Acquisition of localization confidence for accurate object detection
Jiang, B.; Luo, R.; Mao, J.; Xiao, T.; and Jiang, Y. 2018 · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T.; Maeda, S.-i.; Koyama, M.; and Ishii, S. 2018 · 2018
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The open images dataset v4
Kuznetsova, A.; Rom, H.; Alldrin, N.; Uijlings, J.; Krasin, I.; Pont-Tuset, J.; Kamali, S.; Popov, S.; Malloci, M.; Kolesnikov, A.; et al. 2020 · 2020
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Learning from noisy anchors for one-stage object detection
Li, H.; Wu, Z.; Zhu, C.; Xiong, C.; Socher, R.; and Davis, L. S. 2020 · 2020
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Offset bin classification network for accurate object detection
Qiu, H.; Li, H.; Wu, Q.; and Shi, H. 2020 · 2020
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Efficient object detection in large images using deep reinforcement learning
Uzkent, B.; Yeh, C.; and Ermon, S. 2020 · 2020
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Side-aware boundary localization for more precise object detection
Wang, J.; Zhang, W.; Cao, Y.; Chen, K.; Pang, J.; Gong, T.; Shi, J.; Loy, C. C.; and Lin, D. 2020 · 2020
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Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection
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Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2018 · 2018
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Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D.; Carlini, N.; Goodfellow, I.; Papernot, N.; Oliver, A.; and Raffel, C. 2019 · 2019
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Autoaugment: Learning augmentation policies from data
Cubuk, E. D.; Zoph, B.; Mane, D.; Vasudevan, V.; and Le, Q. V. 2019 · 2019
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Consistency-based Semi-supervised Learning for Object Detection
Jeong, J.; Lee, S.; Kim, J.; and Kwak, N. 2019 · 2019
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Autofocus: Efficient multi-scale inference
Najibi, M.; Singh, B.; and Davis, L. S. 2019 · 2019
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FCOS: Fully Convolutional One-Stage Object Detection
Tian, Z.; Shen, C.; Chen, H.; and He, T. 2019 · 2019
Cited alongside, same era.
Detectron2
Wu, Y.; Kirillov, A.; Massa, F.; Lo, W.-Y.; and Girshick, R. 2019 · 2019
Cited alongside, same era.
Zhang, S.; Chi, C.; Yao, Y.; Lei, Z.; and Li, S. Z. 2020 · 2020
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Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
Cai, Z.; Ravichandran, A.; Maji, S.; Fowlkes, C.; Tu, Z.; and Soatto, S. 2021 · 2021
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Evaluating Large-Vocabulary Object Detectors: The Devil is in the Details
Dave, A.; Dollár, P.; Ramanan, D.; Kirillov, A.; and Girshick, R. 2021 · 2021
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Unbiased teacher for semi-supervised object detection
Liu, Y.-C.; Ma, C.-Y.; He, Z.; Kuo, C.-W.; Chen, K.; Zhang, P.; Wu, B.; Kira, Z.; and Vajda, P. 2021 · 2021
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Humble Teachers Teach Better Students for Semi-Supervised Object Detection
Tang, Y.; Chen, W.; Luo, Y.; and Zhang, Y. 2021 · 2021
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Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection
Wang, Z.; Li, Y.; Guo, Y.; Fang, L.; and Wang, S. 2021 · 2021
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Interactive Self-Training with Mean Teachers for Semi-supervised Object Detection
Yang, Q.; Wei, X.; Wang, B.; Hua, X.-S.; and Zhang, L. 2021 · 2021
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Learning to match anchors for visual object detection
Zhang, X.; Wan, F.; Liu, C.; Ji, X.; and Ye, Q. 2021 · 2021
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Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework
Zhou, Q.; Yu, C.; Wang, Z.; Qian, Q.; and Li, H. 2021 · 2021
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