Transcription of Feature Pyramid Networks for Object Detection
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Feature Pyramid Networks for Object Detection
Feature Pyramid Networks for Object DetectionTsung-Yi Lin1,2, Piotr Doll ar1, Ross Girshick1,Kaiming He1, Bharath Hariharan1, and Serge Belongie21 Facebook AI Research (FAIR)2 Cornell University and Cornell TechAbstractFeature pyramids are a basic component in recognitionsystems for detecting objects at different scales. But recentdeep learning Object detectors have avoided Pyramid rep-resentations, in part because they are compute and memoryintensive. In this paper, we exploit the inherent multi-scale,pyramidal hierarchy of deep convolutional Networks to con-struct Feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed forbuilding high-level semantic Feature maps at all scales. Thisarchitecture, called a Feature Pyramid Network (FPN),shows significant improvement as a generic Feature extrac-tor in several applications.
for face detection, and Stacked Hourglass networks [26] for keypoint estimation. Ghiasi et al. [8] present a Lapla-cian pyramid presentation for FCNs to progressively refine segmentation. Although these methods adopt architectures with pyramidal shapes, they are unlike featurized image pyramids [5, 7, 34] where predictions are made indepen-
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