Conditional DETR for Fast Training Convergence
Open accessAbstract
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty.Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.
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Conditional queries used in DAB-DETR do not suit DINO and are not included in the final model.
“In addition, we find the conditional queries [25] used in DAB-DETR does not suit our model and we do not include them in our final model.”
From DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection (2022) · cites this paper as [25]
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Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection
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Object Detection with Transformers: A Review
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Spatial Residual for Underwater Object Detection
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MS-DETR: Efficient DETR Training with Mixed Supervision
Zhao, C., Sun, Y., Wang, W., Chen, Q., Ding, E., Yang, Y., & Wang, J. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01611 · Cited by 90 · see citing works
Self-citationComprehensive review of recent developments in visual object detection based on deep learning
Edozie, E., Shuaibu, A. N., John, U. K., & Sadiq, B. O. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11284-w · Cited by 90 · see citing works
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
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Self-citationBoosting Object Detection with Zero-Shot Day-Night Domain Adaptation
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Temporally Efficient Vision Transformer for Video Instance Segmentation
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DESTR: Object Detection with Split Transformer
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Action Quality Assessment with Temporal Parsing Transformer
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AG-YOLO: A Rapid Citrus Fruit Detection Algorithm with Global Context Fusion
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Enhanced Training of Query-Based Object Detection via Selective Query Recollection
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Exploring Predicate Visual Context in Detecting of Human–Object Interactions
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Self-citationFS-DETR: Few-Shot DEtection TRansformer with prompting and without re-training
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Self-citationCascade-DETR: Delving into High-Quality Universal Object Detection
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CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection
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SPTS v2: Single-Point Scene Text Spotting
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DS-DETR: A Model for Tomato Leaf Disease Segmentation and Damage Evaluation
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DDOD: Dive Deeper into the Disentanglement of Object Detector
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- Authors
- Meng, D., Chen, X., Fan, Z., Zeng, G., Li, H., Yuan, Y., Sun, L., & Wang, J.
- Affiliations
- University of Science and Technology of China (CN)
- Peking University (CN)
- Microsoft (United States) (US)
- Microsoft Research Asia (China) (CN)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 3631-3640
- OpenAlex
- W3194329900
- Cited by
- 1,055
- References
- 59
- Citations by year
- 2026: 222 · 2025: 331 · 2024: 263 · 2023: 174 · 2022: 61 · 2021: 4
- FWCI
- 41.05
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Research and Development
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- DETR, object detection, cross-attention, training convergence, content embedding, spatial localization, object classification, transformer encoder-decoder
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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