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Learning pyramidal feature representations is crucial for recognizing objectinstances at different scales. Feature Pyramid Network (FPN) is the classicarchitecture to build a feature pyramid with high-level semantics throughout.However, intrinsic defects in feature extraction and fusion inhibit FPN fromfurther aggregating more discriminative features. In this work, we proposeAttention Aggregation based Feature Pyramid Network (A^2-FPN), to improvemulti-scale feature learning through attention-guided feature aggregation. Infeature extraction, it extracts discriminative features bycollecting-distributing multi-level global context features, and mitigates thesemantic information loss due to drastically reduced channels. In featurefusion, it aggregates complementary information from adjacent features togenerate location-wise reassembly kernels for content-aware sampling, andemploys channel-wise reweighting to enhance the semantic consistency beforeelement-wise addition. A^2-FPN shows consistent gains on different instancesegmentation frameworks. By replacing FPN with A^2-FPN in Mask R-CNN, our modelboosts the performance by 2.1% and 1.6% mask AP when using ResNet-50 andResNet-101 as backbone, respectively. Moreover, A^2-FPN achieves an improvementof 2.0% and 1.4% mask AP when integrated into the strong baselines such asCascade Mask R-CNN and Hybrid Task Cascade.

A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation