AWhat changed
Used shared convolutional feature extraction across multiscale sliding-window classification and bounding-box regression to integrate recognition, localization and detection.
Scientific scope
Used shared convolutional feature extraction across multiscale sliding-window classification and bounding-box regression to integrate recognition, localization and detection. Inherits convolutional weight sharing and prior dense-evaluation techniques. Localization contest success is not first place in every classification/detection task. The original fixes feature extraction layers when training the regression head; shared features do not imply a single jointly optimized loss for all heads.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits convolutional weight sharing and prior dense-evaluation techniques. Localization contest success is not first place in every classification/detection task. The original fixes feature extraction layers when training the regression head; shared features do not imply a single jointly optimized loss for all heads.
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.
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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