The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformers
Open accessAbstract
Post-training quantization (PTQ), which only requires a tiny dataset for calibration without end-to-end retraining, is a light and practical model compression technique. Recently, several PTQ schemes for vision transformers (ViTs) have been presented; unfortunately, they typically suffer from non-trivial accuracy degradation, especially in low-bit cases. In this paper, we propose RepQ-ViT, a novel PTQ framework for ViTs based on quantization scale reparameterization, to address the above issues. RepQ-ViT decouples the quantization and inference processes, where the former employs complex quantizers and the latter employs scale-reparameterized simplified quantizers. This ensures both accurate quantization and efficient inference, which distinguishes it from existing approaches that sacrifice quantization performance to meet the target hardware. More specifically, we focus on two components with extreme distributions: post-LayerNorm activations with severe inter-channel variation and post-Softmax activations with power-law features, and initially apply channel-wise quantization and log$\sqrt 2 $ quantization, respectively. Then, we reparameterize the scales to hardware-friendly layer-wise quantization and log2 quantization for inference, with only slight accuracy or computational costs. Extensive experiments are conducted on multiple vision tasks with different model variants, proving that RepQ-ViT, without hyperparameters and expensive reconstruction procedures, can outperform existing strong baselines and encouragingly improve the accuracy of 4-bit PTQ of ViTs to a usable level. Code is available at https://github.com/zkkli/RepQ-ViT.
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“Extending this concept, RepQuant [46] advocates for a per-channel dual clipping strategy that establishes specific numerical upper and lower limits for each channel, aiming for accurate quantization while minimizing bias within the quantization space.”
From Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey (2024) · cites this paper as [46]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“RepQ-ViT [45], RepQuant [46] and LRP-QViT [47] initially employs log √ 2 quantization. This approach is better suited for the properties of attention scores in the activations with power-law distributions. Subsequently, the scales are reparameterized to change the base to 2, enabling bit-shifting operations in inference, which are more hardware-friendly.”
From Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey (2024) · cites this paper as [45]
The cited source says what the claim attributes to it.
In the citing paper
“RepQ-ViT [45], RepQuant [46] and LRP-QViT [47] initially employs log √ 2 quantization. This approach is better suited for the properties of attention scores in the activations with power-law distributions. Subsequently, the scales are reparameterized to change the base to 2, enabling bit-shifting operations in inference, which are more hardware-friendly.”
From Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey (2024) · cites this paper as [46]
The cited source says what the claim attributes to it.
In the citing paper
“RepQ-ViT [45] proposes the Scale Reparam method for Post-LayerNorm activations involving a process that begins with applying channel-wise quantization to accurately capture their severe inter-channel variations. This initial step is followed by a critical reparameterization: transforming the channel-wise quantization scales and zero points into a more hardware-friendly layer-wise format. In”
From Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey (2024) · cites this paper as [45]
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Citations
Cites
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
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
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2020) · arXiv · doi:10.48550/arxiv.2010.04159 · Cited by 6,027 · cited 2×
“In addition, ViTs have also been successfully applied to high-level applications, such as object detection [2, 38] and semantic segmentation [28].”
MethodHan (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., & Kalenichenko, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00286 · Cited by 4,114
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., & Dollár, P. (2014) · arXiv · doi:10.48550/arxiv.1405.0312 · Cited by 2,579
Research works printed but not linked: 3
- 10.5555/2999134.2999257 · DOI
- Wang, Towards accurate post-training network quantization via bitsplit and stitching, 9847
- Yao, Hawq-v3: Dyadic neural network quantization, 11875
Cited by
Sorted by citations
I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference
Li, Z., & Gu, Q. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01565 · Cited by 141
“Despite the promising performance, ViTs’ complicated architectures with large memory footprints and computational overheads is intolerable in real-world applications [17, 43, 47, 27], especially in time/resource-constrained scenarios.”
BackgroundMethodSelf-citationNoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
Liu, Y., Yang, H., Dong, Z., Keutzer, K., Du, L., & Zhang, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01946 · Cited by 60
ViTs as backbones: Leveraging vision transformers for feature extraction
Elharrouss, O., Himeur, Y., Mahmood, Y., Alrabaee, S., Ouamane, A., Bensaali, F., Bechqito, Y., & Chouchane, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102951 · Cited by 69
Saha, S., & Xu, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130417 · Cited by 85
PTQ4SAM: Post-Training Quantization for Segment Anything
Lv, C., Chen, H., Guo, J., Guo, J., Guo, J., Ding, Y., Liu, X., Liu, X., & Liu, X. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01509 · Cited by 35
Metadata
- Authors
- Li, Z., Xiao, J., Yang, L., & Gu, Q.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Pages
- 17181-17190
- OpenAlex
- W4390873673
- Cited by
- 127
- References
- 38
- Citations by year
- 2026: 39 · 2025: 59 · 2024: 26 · 2023: 3
- FWCI
- 13.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Keywords
- post-training quantization, vision transformer, model compression, layer-wise quantization, softmax activation, low-bit quantization, inference efficiency
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