NASformer: Neural architecture search for vision transformer
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Methods such as NASformer have better accuracy and fewer parameters than manually designed models such as Swin and DeiT.
“Methods such as NASformer [212] have better accuracy and fewer parameters than manually designed models such as Swin [7] and DeiT [52].”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [212]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534 · see citing works
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
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 · see citing works
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A Survey of Techniques for Optimizing Transformer Inference
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154 · see citing works
- Authors
- Ni, B., Meng, G., Xiang, S., & Pan, C.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Meng, Gaofeng
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2022
- Pages
- 47-61
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4285113377
- Cited by
- 9
- References
- 3
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 1 · 2022: 2
- FWCI
- 0.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- vision transformer, neural architecture search, hierarchical vision transformer, one-shot NAS, parameter-free attention, long-range dependencies, dense prediction, image classification, object detection, semantic segmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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