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

The DOI matches its Crossref record; 56 of the 59 references it cites were found in a registry or an index.

Conditional DETR for Fast Training Convergence

Open access
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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  1. kong, 2019, Foveabox: Beyond anchor-based object detector, CoRR

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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)
ORCID
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
Licence
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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