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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.
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“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]
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“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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Zhou, Y., & Chao, F. (2024) · International Conference on Cyber Security and Information Engineering (ICCSIE) · doi:10.1145/3689236.3689870
QuantAttack: Exploiting Quantization Techniques to Attack Vision Transformers
Baras, A., Zolfi, A., Elovici, Y., & Shabtai, A. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00655 · Cited by 4
SBQ: Exploiting Significant Bits for Efficient and Accurate Post-Training DNN Quantization
Ling, J., Li, G., Hu, Q., Lin, X., Gu, C., Cheng, J., & Liang, X. (2025) · Design, Automation, and Test in Europe · doi:10.23919/date64628.2025.10992952
Sparselight: Dynamic Gradient-Optimized Softmax for Transformer AccelerationPreprint
Zhang, K., Lan, C., Xu, Y., Li, Z., Tan, W., Re, Y., & Hu, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5372682
Self-citationDong, J., Wu, H., Wang, Z., Lou, W., Zheng, Z., Gong, L., Wang, C., & Zhou, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-99857-7_5 · Cited by 1
Han, M., Wu, J. X., Dong, J., Wang, Y., Liu, H., & Qu, G. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3608002
BitFleX: Exploiting extreme bit-level sparsity via BTD encoding and dynamic pruning
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Zero-shot Quantization of Vision Transformers: Leveraging Multi-model Ensembles and Attention Mixup
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An adaptive outlier correction quantization method for vision Transformers
Li, Z., Lan, C., Zhang, K., Tan, W., Ren, Y. T., & Xiao, J. (2025) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2400994
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Kwon, E., & Rosing, T. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3641538
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Wang, G., Lu, Y., Xia, Z., Wei, X., Shi, L., & Zhang, B. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5693-9_9
Zhou, X., Zhao, S., Ding, R., Shi, J., Nie, J., Chen, L., & Liu, H. (2026) · ENGINEERING Information Technology & Electronic Engineering · doi:10.1631/eng.itee.2025.0081
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Self-citationShao, Y., Li, R., Qiao, P., & Jiang, J. (2026) · International Conference on Electrical Automation and Artificial Intelligence (ICEAAI) · doi:10.1109/iceaai68945.2026.11442452
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
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.
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