Methods › Computer Vision › Vision Transformers › Deformable DETR

Deformable DETR

35 papers tagged archive 2025-07-28

Introduced by Xizhou Zhu et al. in Deformable DETR: Deformable Transformers for End-to-End Object Detection

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Deformable DETR is an object detection method that aims mitigates the slow convergence and high complexity issues of DETR. It combines the best of the sparse spatial sampling of deformable convolution, and the relation modeling capability of Transformers. Specifically, it introduces a deformable attention module, which attends to a small set of sampling locations as a pre-filter for prominent key elements out of all the feature map pixels. The module can be naturally extended to aggregating multi-scale features, without the help of FPN.

PaperSource

Papers archive 2025-07-28

30 shown of 35, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 57 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object Detection28
object-detection22
Object16
Decoder8
2D Object Detection3
Autonomous Driving3
Knowledge Distillation3
Pedestrian Detection3
Semi-Supervised Object Detection3
Few-Shot Object Detection2
GPU2
Instance Segmentation2
Language Modeling2
Language Modelling2
Object Localization2
Optical Flow Estimation2
Real-Time Object Detection2
Region Proposal2
Segmentation2
Transfer Learning2

Usage over time archive 2025-07-28

Papers per year tagged with Deformable DETR: 2020 to 2025, peak 10 10 0 2020: 1 paper 2020 2021: 6 papers 2021 2022: 10 papers 2022 2023: 8 papers 2023 2024: 9 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (35 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Vision TransformersObject Detection Models

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