48 citations · 106 across the 11 of their papers we have counts for
7 papers · 1 filter
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Zewei He, Zixuan Chen, Jinlei Li +5
Recently, deep learning-based methods have dominated image dehazing domain. A multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention bloc…
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
Xinqi Lin, Jingwen He, Ziyan Chen +6
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework. DiffBIR decouples blind image restoration probl…
Neural Super-Resolution for Real-time Rendering with Radiance Demodulation
Jia Li, Ziling Chen, Xiaolong Wu +3
It is time-consuming to render high-resolution images in applications such as video games and virtual reality, and thus super-resolution technologies become increasingly popular fo…
Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions
Zirui Chen, Zhaoyang Zhang, Zhaohui Yang
Recently, big artificial intelligence models (BAIMs) represented by chatGPT have brought an incredible revolution. With the pre-trained BAIMs in certain fields, numerous downstream…
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
Yue Zhen, Sheng Bi, Lu Xing-tong +4
Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks. We propose a task planning method that combines human…
From Data-driven Learning to Physics-inspired Inferring: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE
Zhuoran Xiao, Zhaoyang Zhang, Zirui Chen +3
In this paper, we propose an innovative learning-based channel prediction scheme so as to achieve higher prediction accuracy and reduce the requirements of huge amounts and strict…
Benign Overfitting for Two-layer ReLU Convolutional Neural Networks
Yiwen Kou, Zixiang Chen, Yuanzhou Chen +1
Modern deep learning models with great expressive power can be trained to overfit the training data but still generalize well. This phenomenon is referred to as \textit{benign over…