Gu, Qingyi

I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference

Li, Z., & Gu, Q. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsNumerical Methods and Algorithms

Cited by 141

I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference

Li, Z., & Gu, Q. (2022) · arXiv

Advanced Neural Network ApplicationsNumerical Methods and Algorithms

Cited by 140

RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformers

Li, Z., Xiao, J., Yang, L., & Gu, Q. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 127

Patch Similarity Aware Data-Free Quantization for Vision Transformers

Li, Z., Ma, L., Chen, M., Xiao, J., & Gu, Q. (2022) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 61

PSAQ-ViT V2: Toward Accurate and General Data-Free Quantization for Vision Transformers

Li, Z., Chen, M., Xiao, J., & Gu, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 41

PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers

Li, Z., Chen, M., Xiao, J., & Gu, Q. (2022) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 2

BinaryViT: Towards Efficient and Accurate Binary Vision Transformers

Xiao, J., Li, Z., Yang, L., & Gu, Q. (2023) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 0