Ren and colleagues: Faster R-CNN region-proposal networks

What changed

Learned region proposals using convolutional features shared with the detector, replacing a separate proposal-computation bottleneck.

Scientific scope

Learned region proposals using convolutional features shared with the detector, replacing a separate proposal-computation bottleneck. Distinct from Fast R-CNN's detector training. The later pinned journal revision extends the 2015 paper; timing depends on hardware and network configuration.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Distinct from Fast R-CNN's detector training. The later pinned journal revision extends the 2015 paper; timing depends on hardware and network configuration.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Ren and colleagues: Faster R-CNN region-proposal networks — official research record

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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