The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Conformer: Local Features Coupling Global Representations for Visual Recognition
Open accessAbstract
Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visual transformer, the cascaded self-attention modules can capture long-distance feature dependencies but unfortunately deteriorate local feature details. In this paper, we propose a hybrid network structure, termed Conformer, to take advantage of convolutional operations and self-attention mechanisms for enhanced representation learning. Conformer roots in the Feature Coupling Unit (FCU), which fuses local features and global representations under different resolutions in an interactive fashion. Conformer adopts a concurrent structure so that local features and global representations are retained to the maximum extent. Experiments show that Conformer, under the comparable parameter complexity, outperforms the visual transformer (DeiT-B) by 2.3% on ImageNet. On MSCOCO, it outperforms ResNet-101 by 3.7% and 3.6% mAPs for object detection and instance segmentation, respectively, demonstrating the great potential to be a general backbone network. Code is available at github.com/pengzhiliang/Conformer.
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Cites
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Mobile-Former: Bridging MobileNet and Transformer
Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., & Liu, Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00520 · Cited by 731
A survey of the vision transformers and their CNN-transformer based variants
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Gu, X., See, K. W., Li, P., Shan, K., Wang, Y., Zhao, L., Lim, K. C., & Zhang, N. (2023) · Energy · doi:10.1016/j.energy.2022.125501 · Cited by 373
Self-citationF 2 Trans: High-Frequency Fine-Grained Transformer for Face Forgery Detection
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HRFormer: High-Resolution Transformer for Dense Prediction
Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X., & Wang, J. (2021) · arXiv · doi:10.48550/arxiv.2110.09408 · Cited by 178
Metadata
- Authors
- Peng, Z., Huang, W., Gu, S., Xie, L., Wang, Y., Jiao, J., & Ye, Q.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 357-366
- OpenAlex
- W3160694286
- Cited by
- 827
- References
- 30
- FWCI
- 48.25
- Type
- conference paper
- Language
- en
- Open access
- green
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