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Recently two-stage detectors have surged ahead of single-shot detectors in the accuracy-vs-speed trade-off.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
Earlier work this paper cites.
The PASCAL visual object classes (VOC) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Object detection with discriminatively trained part based models
P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan · 2010
Earlier work this paper cites.
Diagnosing error in object detectors
D. Hoiem, Y. Chodpathumwan, and Q. Dai · 2012
Earlier work this paper cites.
Selective search for object recognition
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders · 2013
Earlier work this paper cites.
Scalable object detection using deep neural networks
D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov · 2014
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.
Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
OverFeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Lecun · 2014
Earlier work this paper cites.
Fast R-CNN
R. Girshick · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
S. R. Kaiming He, Xiangyu Zhang and J. Sun · 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.
Inside-Outside Net: Detecting objects in context with skip pooling and recurrent neural networks
S. Bell, C. L. Zitnick, K. Bala, and R. Girshick · 2016
Earlier work this paper cites.
A unified multi-scale deep convolutional neural network for fast object detection
Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos · 2016
Cited alongside, same era.
Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
Cited alongside, same era.
R-FCN: Object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
SSD: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi · 2016
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
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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YOLO9000: better, faster, stronger
J. Redmon and A. Farhadi · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Fully convolutional instance-aware semantic segmentation
J. D. X. J. Yi Li, Haozhi Qi and Y. Wei · 2017
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https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md , 2018
Detectron model zoo and baselines · 2018
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Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Cited alongside, same era.
Beyond skip connections: Top-down modulation for object detection
A. Shrivastava, R. Sukthankar, J. Malik, and A. Gupta · 2016
Cited alongside, same era.
Soft-NMS – improving object detection with one line of code
N. Bodla, B. Singh, R. Chellappa, and L. S. Davis · 2017
Cited alongside, same era.
BlitzNet: A real-time deep network for scene understanding
N. Dvornik, K. Shmelkov, J. Mairal, and C. Schmid · 2017
Cited alongside, same era.
DSSD : Deconvolutional single shot detector
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 2017
Cited alongside, same era.
Accurate, large minibatch sgd: Training imagenet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
Cited alongside, same era.
https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/MODEL_ZOO.md , 2018
Faster R-CNN and Mask R-CNN in PyTorch 1.0: Model zoo and baselines · 2018
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DensePose: Dense human pose estimation in the wild
R. A. Güler, N. Neverova, and I. Kokkinos · 2018
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Path aggregation network for instance segmentation
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia · 2018
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YOLOv3: An incremental improvement
J. Redmon and A. Farhadi · 2018
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MobileNetV2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Group normalization
Y. Wu and K. He · 2018
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ShuffleNet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2018
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
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