FaPN: Feature-aligned Pyramid Network for Dense Image Prediction
2021
Recent advancements in deep neural networks have made remarkable
leap-forwards in dense image prediction. However, the issue of feature
alignment remains as neglected by most existing approaches for simplicity.
Direct pixel addition between upsampled and local features leads to feature
maps with misaligned contexts that, in turn, translate to mis-classifications
in prediction, especially on object boundaries. In this paper, we propose a
feature alignment module that learns transformation offsets of pixels to
contextually align upsampled higher-level features; and another feature
selection module to emphasize the lower-level features with rich spatial
details. We then integrate these two modules in a top-down pyramidal
architecture and present the Feature-aligned Pyramid Network (FaPN). Extensive
experimental evaluations on four dense prediction tasks and four datasets have
demonstrated the efficacy of FaPN, yielding an overall improvement of 1.2 - 2.6
points in AP / mIoU over FPN when paired with Faster / Mask R-CNN. In
particular, our FaPN achieves the state-of-the-art of 56.7% mIoU on ADE20K when
integrated within Mask-Former. The code is available from
https://github.com/EMI-Group/FaPN.
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