42 citations · 71 across the 3 of their papers we have counts for
5 papers
QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
Haotong Qin, Xudong Ma, Xianglong Liu +4
Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited devices. However, when SR m…
BiVM: Accurate Binarized Neural Network for Efficient Video Matting
Haotong Qin, Xianglong Liu, Xudong Ma +4
Deep neural networks for real-time video matting suffer significant computational limitations on edge devices, hindering their adoption in widespread applications such as online co…
An Empirical Study of Qwen3 Quantization
Xingyu Zheng, Yuye Li, Haoran Chu +7
The Qwen series has emerged as a leading family of open-source Large Language Models (LLMs), demonstrating remarkable capabilities in natural language understanding tasks. With the…
An empirical study of LLaMA3 quantization: from LLMs to MLLMs
Wei Huang, Xingyu Zheng, Xudong Ma +7
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the…
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
Haotong Qin, Xudong Ma, Xingyu Zheng +6
The LoRA-finetuning quantization of LLMs has been extensively studied to obtain accurate yet compact LLMs for deployment on resource-constrained hardware. However, existing methods…