Feature Pyramid Blocks

An Recursive Feature Pyramid (RFP) builds on top of the Feature Pyramid Networks (FPN) by incorporating extra feedback connections from the FPN layers into the bottom-up backbone layers. Unrolling the recursive structure to a sequential implementation, we obtain a backbone for object detector that looks at the images twice or more. Similar to the cascaded detector heads in Cascade R-CNN trained with more selective examples, an RFP recursively enhances FPN to generate increasingly powerful representations. Resembling Deeply-Supervised Nets, the feedback connections bring the features that directly receive gradients from the detector heads back to the low levels of the bottom-up backbone to speed up training and boost performance.

Source: DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Instance Segmentation 1 25.00%
Object Detection 1 25.00%
Panoptic Segmentation 1 25.00%
Semantic Segmentation 1 25.00%

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