9 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…
Deep Hashing with Semantic Hash Centers for Image Retrieval
Li Chen, Rui Liu, Yuxiang Zhou +3
Deep hashing is an effective approach for large-scale image retrieval. Current methods are typically classified by their supervision types: point-wise, pair-wise, and list-wise. Re…
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…
Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data
Xudong Ma
Training diffusion models requires large datasets. However, acquiring large volumes of high-quality data can be challenging, for example, collecting large numbers of high-resolutio…
Clustering Properties of Self-Supervised Learning
Xi Weng, Jianing An, Xudong Ma +5
Self-supervised learning (SSL) methods via joint embedding architectures have proven remarkably effective at capturing semantically rich representations with strong clustering prop…
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…