Cited by 1,055Advanced Neural Network ApplicationsVisual Attention and Saliency DetectionAdvanced Image and Video Retrieval Techniques

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Conditional DETR for Fast Training Convergence

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Meng, Depu, Chen, Xiaokang, Fan, Zejia, Zeng, Gang, Li, Houqiang, Yuan, Yuhui, Sun, Lei, Wang, Jingdong·2021·2021 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv48922.2021.00363

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Abstract

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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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)
ORCID: Meng, Depu
Chen, Xiaokang
Fan, Zejia
Zeng, Gang
Li, Houqiang
Yuan, Yuhui
Wang, Jingdong
Venue: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2021-10
Pages: 3631-3640
DOI: 10.1109/iccv48922.2021.00363
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
Licence: cc-by
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Conditional DETR for Fast Training Convergence

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