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

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

What is recorded here?

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

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Distinct from Fast R-CNN's detector training. The later pinned journal revision extends the 2015 paper; timing depends on hardware and network configuration.

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People and systems

Shaoqing Ren; Kaiming He; Ross Girshick; Jian Sun

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.

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English catalogue narrative

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

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

Scientific scope

Distinct from Fast R-CNN's detector training. The later pinned journal revision extends the 2015 paper; timing depends on hardware and network configuration.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Shaoqing Ren; Kaiming He; Ross Girshick; Jian Sun

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.

Original sources