The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
ResNeSt: Split-Attention Networks
Open accessAbstract
The ability to learn richer network representations generally boosts the performance of deep learning models. To improve representation-learning in convolutional neural networks, we present a multi-branch architecture, which applies channel-wise attention across different network branches to leverage the complementary strengths of both feature-map attention and multi-path representation. Our proposed Split-Attention module provides a simple and modular computation block that can serve as a drop-in replacement for the popular residual block, while producing more diverse representations via cross-feature interactions. Adding a Split-Attention module into the architecture design space of RegNet-Y and FBNetV2 directly improves the performance of the resulting network. Replacing residual blocks with our Split-Attention module, we further design a new variant of the ResNet model, named ResNeSt, which outperforms EfficientNet in terms of the accuracy/latency trade-off.
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Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259
“Moreover, with years of rapid developments, there have been many well-engineered CNN backbones such as Res2Net [17], EfficientNet [61], and ResNeSt [79].”
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“ResNeSt [115] also adopts this attention mechanism to improve the CNN backbone in a more general way, giving excellent results on ResNet [146] and ResNeXt [176].”
ResultVision Transformers for Dense Prediction
Ranftl, R., Bochkovskiy, A., & Koltun, V. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01196 · Cited by 2,373
Metadata
- Authors
- Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Lin, H., Zhang, Z., Sun, Y., He, T., Mueller, J., Manmatha, R., Li, M., & Smola, A.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 2735-2745
- OpenAlex
- W3016719260
- Cited by
- 1,648
- References
- 84
- FWCI
- 114.73
- Type
- conference paper
- Language
- en
- Open access
- green
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