Machine Intelligence Research

目录

Vol.20, No.6

MIR第六期已于12月正式出版，来自ETH Zurich、清华大学、哈尔滨工业大学、华南理工大学等海内外著名高校的学者们带来了肺癌影像组学中的机器学习、实用图像盲去噪、单目深度估计等前沿内容，全文免费下载，欢迎阅读！

Machine Learning in Lung Cancer Radiomics

Jiaqi Li, Zhuofeng Li, Lei Wei, Xuegong Zhang

https://link.springer.com/article/10.1007/s11633-022-1364-x

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1364-x

中文导读：

综述 | 清华张学工教授: 肺癌影像组学中的机器学习 

综述 | 西北工业大学

Rolling Shutter Camera: Modeling, Optimization and Learning

Bin Fan, Yuchao Dai, Mingyi He

https://link.springer.com/article/10.1007/s11633-022-1399-z

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1399-z

综述 | 浙江大学&之江实验室

State of the Art on Deep Learning-enhanced Rendering Methods

Qi Wang, Zhihua Zhong, Yuchi Huo, Hujun Bao, Rui Wang

https://link.springer.com/article/10.1007/s11633-022-1400-x

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1400-x

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

Kai Zhang, Yawei Li, Jingyun Liang, Jiezhang Cao, Yulun Zhang, Hao Tang, Deng-Ping Fan, Radu Timofte, Luc Van Gool

https://link.springer.com/article/10.1007/s11633-023-1466-0

https://www.mi-research.net/en/article/doi/10.1007/s11633-023-1466-0

中文导读：

Luc Van Gool团队 | 通过Swin-Conv-UNet和数据合成实现实用图像盲去噪 

DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation

Zhenyu Li, Zehui Chen, Xianming Liu, Junjun Jiang

https://link.springer.com/article/10.1007/s11633-023-1458-0

https://www.mi-research.net/en/article/doi/10.1007/s11633-023-1458-0

中文导读：

哈工大江俊君团队 | DepthFormer: 利用长程关联和局部信息进行精确的单目深度估计 

OTB-morph: One-time Biometrics via Morphing

Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez, Aythami Morales, Ignacio Serna

https://link.springer.com/article/10.1007/s11633-023-1432-x

https://www.mi-research.net/en/article/doi/10.1007/s11633-023-1432-x

MVContrast: Unsupervised Pretraining for Multi-view 3D Object Recognition

Luequan Wang, Hongbin Xu, Wenxiong Kang

https://link.springer.com/article/10.1007/s11633-023-1430-z

https://www.mi-research.net/en/article/doi/10.1007/s11633-023-1430-z

Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory

Zhuorong Li, Yunqi Tang

https://link.springer.com/article/10.1007/s11633-022-1403-7

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1403-7

Multitask Learning with Multiscale Residual Attention for Brain Tumor Segmentation and Classification

Gaoxiang Li, Xiao Hui, Wenjing Li, Yanlin Luo

https://link.springer.com/article/10.1007/s11633-022-1392-6

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1392-6

Mask Distillation Network for Conjunctival Hyperemia Severity Classification

Mingchao Li, Kun Huang, Xiao Ma, Yuexuan Wang, Wen Fan, Qiang Chen

https://link.springer.com/article/10.1007/s11633-022-1385-5

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1385-5

Shallow Feature-driven Dual-edges Localization Network for Weakly Supervised Localization

Wenjun Hui, Guanghua Gu, Bo Wang

https://link.springer.com/article/10.1007/s11633-022-1368-6

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1368-6

Effective Model Compression via Stage-wise PruningEffective Model Compression via Stage-wise Pruning

Ming-Yang Zhang, Xin-Yi Yu, Lin-Lin Ou

https://link.springer.com/article/10.1007/s11633-022-1357-9

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1357-9

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∨关于Machine Intelligence Research

Machine Intelligence Research（简称MIR，原刊名International Journal of Automation and Computing）由中国科学院自动化研究所主办，于2022年正式出版。MIR立足国内、面向全球，着眼于服务国家战略需求，刊发机器智能领域最新原创研究性论文、综述、评论等，全面报道国际机器智能领域的基础理论和前沿创新研究成果，促进国际学术交流与学科发展，服务国家人工智能科技进步。期刊入选"中国科技期刊卓越行动计划"，已被ESCI、EI、Scopus、中国科技核心期刊、CSCD等数据库收录。