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In object detection, an intersection over union (IoU) threshold is required to define positives and negatives.
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S. Gidaris and N. Komodakis · 2015
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R. B. Girshick · 2015
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Learning both weights and connections for efficient neural network
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H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua · 2015
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Faster R-CNN: towards real-time object detection with region proposal networks
S. Ren, K. He, R. B. Girshick, and J. Sun · 2015
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You only look once: Unified, real-time object detection
J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi · 2016
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Training region-based object detectors with online hard example mining
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CRAFT objects from images
B. Yang, J. Yan, Z. Lei, and S. Z. Li · 2016
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A unified multi-scale deep convolutional neural network for fast object detection
Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos · 2016
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Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
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R-FCN: object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
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Attend refine repeat: Active box proposal generation via in-out localization
S. Gidaris and N. Komodakis · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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S. Zagoruyko, A. Lerer, T. Lin, P. O. Pinheiro, S. Gross, S. Chintala, and P. Dollár · 2016
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Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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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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Learning chained deep features and classifiers for cascade in object detection
W. Ouyang, K. Wang, X. Zhu, and X. Wang · 2017
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