The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Deep Layer Aggregation
Open accessAbstract
Visual recognition requires rich representations that span levels from low to high, scales from small to large, and resolutions from fine to coarse. Even with the depth of features in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representations improves inference of what and where. Architectural efforts are exploring many dimensions for network backbones, designing deeper or wider architectures, but how to best aggregate layers and blocks across a network deserves further attention. Although skip connections have been incorporated to combine layers, these connections have been "shallow" themselves, and only fuse by simple, one-step operations. We augment standard architectures with deeper aggregation to better fuse information across layers. Our deep layer aggregation structures iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters. Experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing branching and merging schemes.
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Cites
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Cited by
YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
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“Another mainstream real-time object detectors are developed for GPU [81, 21, 97], they mostly use ResNet [26], DarkNet [63], or DLA [87], and then use the CSPNet [80] strategy to optimize the architecture.”
MethodYOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2022) · arXiv · doi:10.48550/arxiv.2207.02696 · Cited by 13,205
“Another mainstream real-time object detectors are developed for GPU [81, 21, 97], they mostly use ResNet [26], DarkNet [63], or DLA [87], and then use the CSPNet [80] strategy to optimize the architecture.”
MethodBDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00271 · Cited by 2,641
Self-citationNAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Ghiasi, G., Lin, T.-Y., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00720 · Cited by 2,099
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
Zhang, Y., Wang, C., Wang, X., Zeng, W., & Liu, W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01513-4 · Cited by 1,861
Metadata
- Authors
- Yu, F., Wang, D., Shelhamer, E., & Darrell, T.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 2403-2412
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963323244
- Cited by
- 1,516
- References
- 20
- FWCI
- 44.23
- Type
- conference paper
- Language
- en
- Open access
- green
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