  As one of the prevalent components, Feature Pyramid Network (FPN) is widely
used in current object detection models for improving multi-scale object
detection performance. However, its feature fusion mode is still in a
misaligned and local manner, thus limiting the representation power. To address
the inherit defects of FPN, a novel architecture termed Content-Augmented
Feature Pyramid Network (CA-FPN) is proposed in this paper. Firstly, a Global
Content Extraction Module (GCEM) is proposed to extract multi-scale context
information. Secondly, lightweight linear spatial Transformer connections are
added in the top-down pathway to augment each feature map with multi-scale
features, where a linearized approximate self-attention function is designed
for reducing model complexity. By means of the self-attention mechanism in
Transformer, there is no longer need to align feature maps during feature
fusion, thus solving the misaligned defect. By setting the query scope to the
entire feature map, the local defect can also be solved. Extensive experiments
on COCO and PASCAL VOC datasets demonstrated that our CA-FPN outperforms other
FPN-based detectors without bells and whistles and is robust in different
settings.
