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For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high efficiency.
Rapid object detection using a boosted cascade of simple features
P. A. Viola and M. J. Jones · 2001
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Edge boxes: Locating object proposals from edges
C. L. Zitnick and P. Dollár · 2007
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The pascal visual object classes (VOC) challenge
M. Everingham, L. J. V. Gool, C. K. I. Williams, J. M. Winn, and A. Zisserman · 2010
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Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan · 2010
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Some improvements on deep convolutional neural network based image classification
A. G. Howard · 2013
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Selective search for object recognition
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders · 2013
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Scalable object detection using deep neural networks
D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. B. 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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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell · 2014
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Microsoft COCO: common objects in context
T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
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Object detection via a multi-region and semantic segmentation-aware CNN model
S. Gidaris and N. Komodakis · 2015
Earlier work this paper cites.
Fast R-CNN
R. B. Girshick · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Learning to segment object candidates
P. H. O. Pinheiro, R. Collobert, and P. Dollár · 2015
Cited alongside, same era.
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. S. Bernstein, A. C. Berg, and F. Li · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
S. Bell, C. L. Zitnick, K. Bala, and R. B. Girshick · 2016
Cited alongside, same era.
A unified multi-scale deep convolutional neural network for fast object detection
Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos · 2016
Cited alongside, same era.
R-FCN: object detection via region-based fully convolutional networks
Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
Closest in time.
The Leaderboard of the PASCAL Visual Object Classes Challenge 2012 (VOC2012)
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2017
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2017
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2017
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DSSD : Deconvolutional single shot detector
C. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 2017
Closest in time.
Speed/accuracy trade-offs for modern convolutional object detectors
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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.
Hypernet: Towards accurate region proposal generation and joint object detection
T. Kong, A. Yao, Y. Chen, and F. Sun · 2016
Cited alongside, same era.
SSD: single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C. Fu, and A. C. Berg · 2016
Cited alongside, same era.
Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2016
Cited alongside, same era.
Learning to refine object segments
P. O. Pinheiro, T. Lin, R. Collobert, and P. Dollár · 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.
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy · 2017
Closest in time.
RON: reverse connection with objectness prior networks for object detection
T. Kong, F. Sun, A. Yao, H. Liu, M. Lu, and Y. Chen · 2017
Closest in time.
ME R-CNN: multi-expert region-based CNN for object detection
H. Lee, S. Eum, and H. Kwon · 2017
Closest in time.
Feature pyramid networks for object detection
T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
Closest in time.
Focal loss for dense object detection
T. Lin, P. Goyal, R. B. Girshick, K. He, and P. Dollár · 2017
Closest in time.
Faster R-CNN: towards real-time object detection with region proposal networks
S. Ren, K. He, R. B. Girshick, and J. Sun · 2017
Closest in time.
DSOD: learning deeply supervised object detectors from scratch
Z. Shen, Z. Liu, J. Li, Y. Jiang, Y. Chen, and X. Xue · 2017
Closest in time.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
Closest in time.
A-fast-rcnn: Hard positive generation via adversary for object detection
X. Wang, A. Shrivastava, and A. Gupta · 2017
Closest in time.
Aggregated residual transformations for deep neural networks
S. Xie, R. B. Girshick, P. Dollár, Z. Tu, and K. He · 2017
Closest in time.
Detecting face with densely connected face proposal network
S. Zhang, X. Zhu, Z. Lei, H. Shi, X. Wang, and S. Z. Li · 2017
Closest in time.
Faceboxes: A CPU real-time face detector with high accuracy
S. Zhang, X. Zhu, Z. Lei, H. Shi, X. Wang, and S. Z. Li · 2017
Closest in time.
Couplenet: Coupling global structure with local parts for object detection
Y. Zhu, C. Zhao, J. Wang, X. Zhao, Y. Wu, and H. Lu · 2017
Closest in time.