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Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Selective search for object recognition
J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 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.
Motchallenge 2015: Towards a benchmark for multi-target tracking
L. Leal-Taixé, A. Milan, I. D. Reid, S. Roth, and K. Schindler · 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
Earlier work this paper cites.
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
Earlier work this paper cites.
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.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
A note on the triangle inequality for the jaccard distance
S. Kosub · 2016
Cited alongside, same era.
The visual object tracking vot2016 challenge results
M. Kristan and · 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.
Optimizing intersection-over-union in deep neural networks for image segmentation
M. A. Rahman and Y. Wang · 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.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Later among the works it cites.
Scene parsing through ade20k dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
Later among the works it cites.
Augmented reality meets computer vision: Efficient data generation for urban driving scenes
H. Alhaija, S. Mustikovela, L. Mescheder, A. Geiger, and C. Rother · 2018
Later among the works it cites.
Acquisition of localization confidence for accurate object detection
B. Jiang, R. Luo, J. Mao, T. Xiao, and Y. Jiang · 2018
Later among the works it cites.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2018
Later among the works it cites.
Path aggregation network for instance segmentation
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia · 2018
Later among the works it cites.
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J. Redmon and A. Farhadi · 2016
Cited alongside, same era.
Unitbox: An advanced object detection network
J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang · 2016
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
M. B. A. R. T. Matthew and B. Blaschko · 2018
Later among the works it cites.
Yolov3: An incremental improvement
J. Redmon and A. Farhadi · 2018
Later among the works it cites.