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In this paper, we first investigate why typical two-stage methods are not as fast as single-stage, fast detectors like YOLO and SSD.
A wavelet tour of signal processing
S. Mallat · 1999
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Selective search for object recognition
J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders · 2013
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Multiscale combinatorial grouping
P. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik · 2014
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
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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
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Edge boxes: Locating object proposals from edges
C. L. Zitnick and P. Dollár · 2014
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Fast r-cnn
R. Girshick · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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 · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 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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Dssd: Deconvolutional single shot detector
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 2017
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K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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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
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F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
Cited alongside, same era.
R-fcn: Object detection via region-based fully convolutional networks
Y. Li, K. He, J. Sun, et al · 2016
Cited alongside, same era.
Ms coco api. https://github.com/pdollar/coco
T.-Y. Lin and P. Dollár · 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 · 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. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Yolo9000: Better, faster, stronger
J. Redmon and A. Farhadi · 2016
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 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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Large kernel matters–improve semantic segmentation by global convolutional network
C. Peng, X. Zhang, G. Yu, G. Luo, and J. Sun · 2017
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Object detection networks on convolutional feature maps
S. Ren, K. He, R. Girshick, X. Zhang, and J. Sun · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Denet: Scalable real-time object detection with directed sparse sampling
L. Tychsen-Smith and L. Petersson · 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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Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
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