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Object detection remains an active area of research in the field of computer vision, and considerable advances and successes has been achieved in this area through the design of deep convolutional neural networks for tackling object detection.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Earlier work this paper cites.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
SSD: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Earlier work this paper cites.
YOLO9000: better, faster, stronger
J. Redmon and A. Farhadi · 2016
Earlier work this paper cites.
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Earlier work this paper cites.
B. Wu, F. Iandola, P. H. Jin, and K. Keutzer · 2016
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollar, and R. Girshick · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
Later among the works it cites.
Mnasnet: Platform-aware neural architecture search for mobile
M. Tan, B. Chen, R. Pang, V. Vasudevan, and Q. V. Le · 2018
Later among the works it cites.
A. Wong · 2018
Later among the works it cites.
Ferminets: Learning generative machines to generate efficient neural networks via generative synthesis
A. Wong, M. J. Shafiee, B. Chwyl, and F. Li · 2018
Later among the works it cites.
Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search
X. Chu, B. Zhang, R. Xu, and J. Li · 2019
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Cited alongside, same era.
Fast YOLO: A fast you only look once system for real-time embedded object detection in video
M. J. Shafiee, B. Chywl, F. Li, and A. Wong · 2017
Cited alongside, same era.
Yolov3: An incremental improvement
J. Redmon and A. Farhadi · 2018
Cited alongside, same era.
Closest in time.
Squeeze-and-excitation networks
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu · 2019
Closest in time.
A. Wong, Z. Q. Lin, and B. Chwyl · 2019
Closest in time.