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Deformable DETR: reference-guided sparse multi-scale attention

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.

What is recorded here?

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.

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

Scientific scope

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.

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People and systems

Xizhou Zhu; Weijie Su; Lewei Lu; Bin Li; Xiaogang Wang; Jifeng Dai

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

Deformable DETR: reference-guided sparse multi-scale attention

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

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.

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

Xizhou Zhu; Weijie Su; Lewei Lu; Bin Li; Xiaogang Wang; Jifeng Dai

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