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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RepQuant 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.
“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]
Section 3.2 (p. 4) supports channel-wise quantization to address inter-channel variation and improve accuracy, using a scale and zero-point for each channel. It does not describe a per-channel dual-clipping strategy, channel-specific upper and lower clipping limits, or minimizing bias in the quantization space. Instead, it describes reparameterizing channel-wise quantization into layer-wise quantization for inference. (LLM, conf=0.88)
Experimental — accuracy not yet measuredRepQ-ViT, RepQuant, and LRP-QViT initially employ log√2 quantization, then reparameterize scales to base 2 to enable bit-shifting operations in inference.
“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]
RepQ-ViT, RepQuant, and LRP-QViT initially employ log√2 quantization, then reparameterize scales to base 2 to enable bit-shifting operations in inference.
“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]
RepQ-ViT proposes the Scale Reparam method for Post-LayerNorm activations, applying channel-wise quantization then reparameterizing channel-wise scales and zero points into a hardware-friendly layer-wise format, and modifying affine factors of LayerNorm and weights of the following layer.
“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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“RepQ-ViT [45], RepQuant [46] and LRP-QViT [47] initially employs log √ 2 quantization.”
MethodDomain aware post training quantization for vision transformers in deployment
Wang, L., Zeng, C., Zhang, M., Wu, J., & Nie, L. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112182 · Cited by 4 · see citing works
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Minimize Quantization Output Error with Bias Compensation
Gong, C., Zheng, H., Hu, M., Zheng, L., Fan, D.-P., Zhang, Y., & Li, T. (2024) · CAAI Artificial Intelligence Research · doi:10.26599/air.2024.9150036 · Cited by 4 · see citing works
T3DNet: Compressing Point Cloud Models for Lightweight 3-D Recognition
Yang, Z., Zhou, Y., Xie, L., & Yang, J. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3487220 · Cited by 4 · see citing works
Quantized Prompt for Efficient Generalization of Vision-Language Models
Hao, T., Ding, X., Feng, J., Yang, Y., Chen, H., & Ding, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72655-2_4 · Cited by 4 · see citing works
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation Smoothing
Dong, Z., & Zhang, S. Q. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00452 · Cited by 4 · see citing works
Advances in edge-cloud collaboration and evolution for large-small models
Wang, Y., Shen, T., Zhang, S., Wu, F., Zhao, Z., Cai, H., Lyu, C., Ma, L., Yang, C., & Wu, F. L. (2024) · Journal of Image and Graphics · doi:10.11834/jig.240011 · Cited by 6 · see citing works
Token-based dynamic bit-width assignment for ViT quantization
Kim, D., Moon, J., Lee, J., Lee, G., Jeon, J., & Ham, B. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112269 · Cited by 3 · see citing works
PoMQ-ViT: Mixed-Precision Quantization Vision Transformer with Pareto Optimization
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Efficient Integer Quantization for Compressed DETR Models
Liu, P., Li, C., Zhang, N., Yang, J., & Wang, L. (2025) · Entropy · doi:10.3390/e27040422 · Cited by 3 · see citing works
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model
Ranjan, N., & Savakis, A. E. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00310 · Cited by 4 · see citing works
HTQ: Exploring the High-Dimensional Trade-Off of mixed-precision quantization
Li, Z., Long, X., Xiao, J., & Gu, Q. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110788 · Cited by 3 · see citing works
Self-citationA 28nm 343.5fps/W Vision Transformer Accelerator with Integer-Only Quantized Attention Block
Lin, C.-C., Lu, W., Huang, P.-T., & Hung-Ming, C. (2024) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas59952.2024.10595969 · Cited by 3 · see citing works
Deep Compression on Segment Anything Model for Efficient Industrial Manufacturing
Zheng, Y., Liu, J., Li, Q., Li, J., & Xi, Z. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3585613 · Cited by 2 · see citing works
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Yang, X., Zhao, T., Wang, H., Ma, W., Zeng, S., Zhu, Z., Ning, X., Yang, H., & Wang, Y. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11133383 · Cited by 2 · see citing works
I&S-ViT: An Inclusive & Stable Method for Post-Training ViTs Quantization
Zhong, Y., Hu, J., Lin, M., Chen, M., & Ji, R. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3610466 · Cited by 7 · see citing works
PTQAT: A Hybrid Parameter-Efficient Quantization Algorithm for 3D Perception Tasks
Wang, X., Lin, Z., Xia, Z., & Wang, Y. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00423 · Cited by 2 · see citing works
Semantic Alignment and Reinforcement for Data-Free Quantization of Vision Transformers
Zhong, Y., Zhou, Y., Zhang, Y., Sui, W., Li, S., Li, Y., Chao, F., & Ji, R. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01160 · Cited by 2 · see citing works
MGRQ: Post-Training Quantization For Vision Transformer With Mixed Granularity Reconstruction
Yang, L., Li, Z., Xiao, J., Gong, H., & Gu, Q. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10648284 · Cited by 3 · see citing works
Self-citationVision transformer quantization with multi-step knowledge distillation
Ranjan, N., & Savakis, A. E. (2024) · Signal processing, sensor fusion, and target recognition. Conference · doi:10.1117/12.3014158 · Cited by 2 · see citing works
Neural Substitution for Branch-Level Network Re-parameterization
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Mixed Non-linear Quantization for Vision Transformers
Kim, G., Lee, J., Park, S., Kwon, Y., & Kim, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91979-4_10 · Cited by 1 · see citing works
Long, Y., Zhao, L., Liu, T., Yuan, Z., Diao, W., Yan, M., & Wang, Z. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3656351 · Cited by 1 · see citing works
Enhancing Generalization in Data-Free Quantization via Mixup-Class Prompting
Park, J., Lee, C., Choi, Y., Park, S., Hong, D., & Choi, J. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00422 · Cited by 1 · see citing works
Exploiting Information Redundancy in Attention Maps for Extreme Quantization of Vision Transformers
Maisonnave, L., Haroun, K., & Pégeot, T. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00425 · Cited by 1 · see citing works
Cao, J., Guo, J., Xiong, W., Luo, H., Wang, J., & Lai, J. (2025) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.1109/fpl68686.2025.00040 · Cited by 1 · see citing works
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INSPIRE: In-Sensor Compressed Weight Retrieval for Enhancing ViT Efficiency at Edge
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D4C: Data-Free Quantization for Contrastive Language-Image Pre-Training Models
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DQDG: Data-Free Quantization With Dual Generators for Keyword Spotting
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Empirical Research On Quantization For 3D Multi-Modal Vit Models
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QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning
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EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models
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Self-citationAdfq-Vit: Activation-Distribution-Friendly Post-Training Quantization for Vision TransformersPreprint
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Robustness-Enhanced Data-Free Quantization for Vision Transformer
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
SBQ: Exploiting Significant Bits for Efficient and Accurate Post-Training DNN Quantization
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Sparselight: Dynamic Gradient-Optimized Softmax for Transformer AccelerationPreprint
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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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NP4Q: Nice Point for Post-training Quantization of Object Detection Models
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An adaptive outlier correction quantization method for vision Transformers
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Quantization Techniques for Resource-Efficient Medical Image Segmentation Across Diverse Modalities
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Triple Sorting-Based Post-Training Quantization for Low-Bit Vision Transformers
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- 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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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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