Deformable ConvNets V2: More Deformable, Better Results

The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to obj...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 9300 - 9308
Hlavní autori:	Zhu, Xizhou, Hu, Han, Lin, Stephen, Dai, Jifeng
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2019
Predmet:	Categorization Convolution Convolutional neural networks Deformable models Deformation Instance segmentation Modulation Object detection Pattern recognition Performance gain Recognition: Detection Retrieval Training
ISSN:	1063-6919
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