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We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection.
Pyramid methods in image processing
Edward H Adelson, Charles H Anderson, James R Bergen, Peter J Burt, and Joan M Ogden · 1984
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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The pascal visual object classes challenge: A retrospective
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Mask r-cnn
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
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Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2017
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Faster R-CNN: towards real-time object detection with region proposal networks
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Path aggregation network for instance segmentation
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Group sampling for scale invariant face detection
Xiang Ming, Fangyun Wei, Ting Zhang, Dong Chen, and Fang Wen · 2019
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Pod: practical object detection with scale-sensitive network
Junran Peng, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, and Junjie Yan · 2019
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Fcos: Fully convolutional one-stage object detection
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