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Recent advancements in deep neural networks have made remarkableleap-forwards in dense image prediction. However, the issue of featurealignment remains as neglected by most existing approaches for simplicity.Direct pixel addition between upsampled and local features leads to featuremaps with misaligned contexts that, in turn, translate to mis-classificationsin prediction, especially on object boundaries. In this paper, we propose afeature alignment module that learns transformation offsets of pixels tocontextually align upsampled higher-level features; and another featureselection module to emphasize the lower-level features with rich spatialdetails. We then integrate these two modules in a top-down pyramidalarchitecture and present the Feature-aligned Pyramid Network (FaPN). Extensiveexperimental evaluations on four dense prediction tasks and four datasets havedemonstrated the efficacy of FaPN, yielding an overall improvement of 1.2 - 2.6points in AP / mIoU over FPN when paired with Faster / Mask R-CNN. Inparticular, our FaPN achieves the state-of-the-art of 56.7% mIoU on ADE20K whenintegrated within Mask-Former. The code is available fromhttps://github.com/EMI-Group/FaPN.

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction