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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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“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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Cited by
Sorted by citations
DETRs Beat YOLOs on Real-time Object Detection
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“Recently, the end-to-end Transformer-based detectors (DETRs) [4, 16, 22, 25, 33, 36, 41, 42] have received extensive attention from the academia due to their streamlined architecture and elimination of hand-crafted components.”
BackgroundMasked-attention Mask Transformer for Universal Image Segmentation
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DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
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DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
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“To address such problems, many methods have been proposed, such as introducing deformable attention [41], decoupling positional and content information [25], providing spatial priors [11,39,37], etc.”
BackgroundMethod
Metadata
- 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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