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Aggregating context information from multiple scales has been proved to be effective for improving accuracy of Single Shot Detectors (SSDs) on object detection.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 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.
Object detection via a multi-region and semantic segmentation-aware cnn model
S. Gidaris and N. Komodakis · 2015
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Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 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
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
S. Bell, C. Lawrence Zitnick, K. Bala, and R. Girshick · 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
Earlier work this paper cites.
Region-based convolutional networks for accurate object detection and segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 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.
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
Dssd: Deconvolutional single shot detector
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 2017
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Enhancement of ssd by concatenating feature maps for object detection
J. Jeong, H. Park, and N. Kwak · 2017
Closest in time.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Closest in time.
Inception single shot multibox detector for object detection
C. Ning, H. Zhou, Y. Song, and J. Tang · 2017
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Accurate single stage detector using recurrent rolling convolution
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y.-W. Tai, and L. Xu · 2017
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Yolo9000: better, faster, stronger
J. Redmon and A. Farhadi · 2016
Cited alongside, same era.
Dual path networks
Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng · 2017
Cited alongside, same era.
Cad: Scale invariant framework for real-time object detection
H. Zhou, Z. Li, C. Ning, and J. Tang
Cited in the paper.
Closest in time.
Stairnet: Top-down semantic aggregation for accurate one shot detection
S. Woo, S. Hwang, and I. S. Kweon · 2017
Closest in time.
Context-aware single-shot detector
W. Xiang, D.-Q. Zhang, V. Athitsos, and H. Yu · 2017
Closest in time.