AWhat changed
Predicted a small set of sampling offsets and attention weights around reference points, using interpolated multi-scale image features to reduce dense-attention cost and improve detector training.
Scientific scope
Predicted a small set of sampling offsets and attention weights around reference points, using interpolated multi-scale image features to reduce dense-attention cost and improve detector training. Inherits deformable convolution and DETR set prediction. Query-predicted sparse sampling is not exact dense-attention equivalence or a theorem of preserved accuracy. Complexity savings assume a bounded sampling count; training comparisons are benchmark-specific.
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 deformable convolution and DETR set prediction. Query-predicted sparse sampling is not exact dense-attention equivalence or a theorem of preserved accuracy. Complexity savings assume a bounded sampling count; training comparisons are benchmark-specific.
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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