READING NOTE: Feature Pyramid Networks for Object Detection
2016-12-15 19:53
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TITLE: Feature Pyramid Networks for Object Detection
AUTHOR: Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie
ASSOCIATION: Facebook AI Research, Cornell University and Cornell Tech
FROM: arXiv:1612.03144
Similar with SSD, predictions are made in different scales. But connects exist between different scales. Though the author calls the connections as lateral connections, it is actually skip connections among different layers, just like what ResNet does.
AUTHOR: Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie
ASSOCIATION: Facebook AI Research, Cornell University and Cornell Tech
FROM: arXiv:1612.03144
CONTRIBUTIONS
A new topdown architecture with lateral connections is developed for building high-level semantic feature maps at all scales is proposed. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications.METHOD
The idea of this work is simple and is illustrated in the following figureSimilar with SSD, predictions are made in different scales. But connects exist between different scales. Though the author calls the connections as lateral connections, it is actually skip connections among different layers, just like what ResNet does.
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