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Recent object detectors use four-coordinate bounding box (bbox) regression to predict object locations.
Shortest connection networks and some generalizations
R. C. Prim · 1957
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Minimum spanning trees and single linkage cluster analysis
J. C. Gower and G. J. Ross · 1969
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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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 · 2013
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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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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 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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Fast r-cnn
R. Girshick · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollár · 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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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Cited alongside, same era.
Instance-sensitive fully convolutional networks
J. Dai, K. He, Y. Li, S. Ren, and J. Sun · 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.
Fully convolutional instance-aware semantic segmentation
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei · 2016
Deep watershed transform for instance segmentation
M. Bai and R. Urtasun · 2017
Later among the works it cites.
Dssd: Deconvolutional single shot detector
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 2017
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Boundary-aware instance segmentation
Z. Hayder, X. He, and M. Salzmann · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Instancecut: from edges to instances with multicut
A. Kirillov, E. Levinkov, B. Andres, B. Savchynskyy, and C. Rother · 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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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.
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Cited alongside, same era.
Pixelwise instance segmentation with a dynamically instantiated network
A. Arnab and P. H. Torr · 2017
Cited alongside, same era.
Sgn: Sequential grouping networks for instance segmentation
S. Liu, J. Jia, S. Fidler, and R. Urtasun · 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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Efficient interactive annotation of segmentation datasets with polygon-rnn++
D. Acuna, H. Ling, A. Kar, and S. Fidler · 2018
Closest in time.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2018
Closest in time.