  Feature pyramids have been proven powerful in image understanding tasks that
require multi-scale features. State-of-the-art methods for multi-scale feature
learning focus on performing feature interactions across space and scales using
neural networks with a fixed topology. In this paper, we propose graph feature
pyramid networks that are capable of adapting their topological structures to
varying intrinsic image structures and supporting simultaneous feature
interactions across all scales. We first define an image-specific superpixel
hierarchy for each input image to represent its intrinsic image structures. The
graph feature pyramid network inherits its structure from this superpixel
hierarchy. Contextual and hierarchical layers are designed to achieve feature
interactions within the same scale and across different scales. To make these
layers more powerful, we introduce two types of local channel attention for
graph neural networks by generalizing global channel attention for
convolutional neural networks. The proposed graph feature pyramid network can
enhance the multiscale features from a convolutional feature pyramid network.
We evaluate our graph feature pyramid network in the object detection task by
integrating it into the Faster R-CNN algorithm. The modified algorithm
outperforms not only previous state-of-the-art feature pyramid-based methods
with a clear margin but also other popular detection methods on both MS-COCO
2017 validation and test datasets.
