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Feature Pyramid Networks: top-down semantic features with lateral fusion

Contribution grade

Multi-scale visual representations · Human-designed research method or resource

Reference score180,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Built semantically enriched feature maps at several backbone resolutions through a top-down pathway and same-resolution lateral additions, reusing one image's convolutional hierarchy.

Scientific scope

Built semantically enriched feature maps at several backbone resolutions through a top-down pathway and same-resolution lateral additions, reusing one image's convolutional hierarchy. Feature pyramids and skip connections predate the method. This construction is not a zero-cost substitute for every image pyramid; scales retain different information and detection effects depend on heads and training. Inherited backbone and proposal methods retain attribution.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Feature pyramids and skip connections predate the method. This construction is not a zero-cost substitute for every image pyramid; scales retain different information and detection effects depend on heads and training. Inherited backbone and proposal methods retain attribution.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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