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CASIA OpenIR

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Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 822-836
作者:  Kai Zhang;  Yawei Li;  Jingyun Liang;  Jiezhang Cao;  Yulun Zhang;  Hao Tang;  Deng-Ping Fan;  Radu Timofte;  Luc Van Gool
Adobe PDF(7952Kb)  |  收藏  |  浏览/下载：6/3  |  提交时间：2024/04/23
Blind image denoising, real image denosing data synthesis, Transformer, image signal processing (ISP) pipeline  
State of the Art on Deep Learning-enhanced Rendering Methods 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 799-821
作者:  Qi Wang;  Zhihua Zhong;  Yuchi Huo;  Hujun Bao;  Rui Wang
Adobe PDF(6540Kb)  |  收藏  |  浏览/下载：15/7  |  提交时间：2024/04/23
Neural rendering, computer graphics, scene representation, rendering, post-processing  
A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 5, 页码: 614-639
作者:  Bolin Gao;  Keke Wan;  Qien Chen;  Zhou Wang;  Rui Li;  Yu Jiang;  Run Mei;  Yinghui Luo;  Keqiang Li
Adobe PDF(12630Kb)  |  收藏  |  浏览/下载：9/3  |  提交时间：2024/04/23
Predictive cruise control (PCC), cloud control system (CCS), cooperative control, efficient operation, intelligent connected vehicle  
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 483-513
作者:  Wei-Chien Wang;  Euijoon Ahn;  Dagan Feng;  Jinman Kim
Adobe PDF(2691Kb)  |  收藏  |  浏览/下载：11/5  |  提交时间：2024/04/23
Self-supervised learning (SSL), contrastive learning, deep learning, medical image analysis, computer vision  
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 447-482
作者:  Xiao Wang;  Guangyao Chen;  Guangwu Qian;  Pengcheng Gao;  Xiao-Yong Wei;  Yaowei Wang;  Yonghong Tian;  Wen Gao
Adobe PDF(3540Kb)  |  收藏  |  浏览/下载：13/3  |  提交时间：2024/04/23
Multi-modal (MM), pre-trained model (PTM), information fusion, representation learning, deep learning  
A Survey on Collaborative DNN Inference for Edge Intelligence 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 3, 页码: 370-395
作者:  Wei-Qing Ren;  Yu-Ben Qu;  Chao Dong;  Yu-Qian Jing;  Hao Sun;  Qi-Hui Wu;  Song Guo
Adobe PDF(2969Kb)  |  收藏  |  浏览/下载：9/3  |  提交时间：2024/04/23
Artificial intelligence (AI), edge intelligence (EI), distributed computing, deep neural network (DNN), collaborative inference  
Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 3, 页码: 335-369
作者:  Rui Jiang;  Ruixiang Zhu;  Hu Su;  Yinlin Li;  Yuan Xie;  Wei Zou
Adobe PDF(9061Kb)  |  收藏  |  浏览/下载：3/0  |  提交时间：2024/04/23
Moving object segmentation (MOS), change detection, background subtraction, deep learning (DL), video understanding  
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 3, 页码: 318-334
作者:  Wai-Chung Kwan;  Hong-Ru Wang;  Hui-Min Wang;  Kam-Fai Wong
Adobe PDF(2211Kb)  |  收藏  |  浏览/下载：0/0  |  提交时间：2024/04/23
Dialogue policy learning (DPL), task-oriented dialogue system (TOD), reinforcement learning (RL), dialogue system, Markov decision process  
Compositional Prompting Video-language Models to Understand Procedure in Instructional Videos 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 2, 页码: 249-262
作者:  Guyue Hu;  Bin He;  Hanwang Zhang
Adobe PDF(2167Kb)  |  收藏  |  浏览/下载：12/6  |  提交时间：2024/04/23
Prompt learning  video-language pretrained models  instructional videos  procedure understanding  knowledge distilling  
A Study of Using Synthetic Data for Effective Association Knowledge Learning 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 2, 页码: 194-206
作者:  Yuchi Liu;  Zhongdao Wang;  Xiangxin Zhou;  Liang Zheng
Adobe PDF(2006Kb)  |  收藏  |  浏览/下载：4/3  |  提交时间：2024/04/23
Multi-object tracking (MOT)  data association  synthetic data  motion simulation  association knowledge learning