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Knowledge Distillation (KD) is a widely-used technology to inherit information from cumbersome teacher models to compact student models, consequently realizing model compression and acceleration.
Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
Li, X.; Wang, W.; Wu, L.; Chen, S.; Hu, X.; Li, J.; Tang, J.; and Yang, J. 2020 · 2006
Earlier work this paper cites.
Distilling Object Detectors with Task Adaptive Regularization
Sun, R.; Tang, F.; Zhang, X.; Xiong, H.; and Tian, Q. 2020 · 2006
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M.; Van Gool, L.; Williams, C. K.; Winn, J.; and Zisserman, A. 2010 · 2010
Earlier work this paper cites.
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
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Romero, A.; Ballas, N.; Kahou, S. E.; Chassang, A.; Gatta, C.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Learning efficient object detection models with knowledge distillation
Chen, G.; Choi, W.; Yu, X.; Han, T.; and Chandraker, M. 2017 · 2017
Cited alongside, same era.
Mask r-cnn
He, K.; Gkioxari, G.; Dollár, P.; and Girshick, R. 2017 · 2017
Cited alongside, same era.
Mimicking very efficient network for object detection
Li, Q.; Jin, S.; and Yan, J. 2017 · 2017
Cited alongside, same era.
YOLO9000: better, faster, stronger
Redmon, J.; and Farhadi, A. 2017 · 2017
Cited alongside, same era.
Cascade r-cnn: Delving into high quality object detection
Cai, Z.; and Vasconcelos, N. 2018 · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
Cited alongside, same era.
Distilling object detectors with fine-grained feature imitation
Wang, T.; Yuan, L.; Zhang, X.; and Feng, J. 2019 · 2019
Later among the works it cites.
Efficientdet: Scalable and efficient object detection
Tan, M.; Pang, R.; and Le, Q. V. 2020 · 2020
Later among the works it cites.
Zhang, S.; Chi, C.; Yao, Y.; Lei, Z.; and Li, S. Z. 2020 · 2020
Later among the works it cites.
General Instance Distillation for Object Detection
Dai, X.; Jiang, Z.; Wu, Z.; Bao, Y.; Wang, Z.; Liu, S.; and Zhou, E. 2021 · 2021
Closest in time.
Distilling Object Detectors via Decoupled Features
Guo, J.; Han, K.; Wang, Y.; Wu, H.; Chen, X.; Xu, C.; and Xu, C. 2021 · 2021
Closest in time.
Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection
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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.
Feature pyramid networks for object detection
Lin, T.-Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; and Belongie, S. 2017a
Cited in the paper.
Focal loss for dense object detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017b
Cited in the paper.
Li, X.; Wang, W.; Hu, X.; Li, J.; Tang, J.; and Yang, J. 2021 · 2021
Closest in time.
Varifocalnet: An iou-aware dense object detector
Zhang, H.; Wang, Y.; Dayoub, F.; and Sunderhauf, N. 2021 · 2021
Closest in time.