Vol.20, No.5

MIR第五期已于10月正式出版，来自苏黎世联邦理工大学、澳大利亚国立大学、穆罕默德·本·扎伊德人工智能大学、清华大学、上海交通大学、南方科技大学、香港中文大学、华南理工大学等海内外著名高校的学者们带来了生成式人工智能系统、智能网联汽车、毫秒级人脸检测器、个性化联邦学习框架等前沿内容，全文免费下载，欢迎阅读！

How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges

Haotong Qin, Ge-Peng Ji, Salman Khan, Deng-Ping Fan, Fahad Shahbaz Khan & Luc Van Gool

https://link.springer.com/article/10.1007/s11633-023-1469-x

https://www.mi-research.net/en/article/doi/10.1007/s11633-023-1469-x

中文导读：

前沿观点 | 谷歌BARD的视觉理解能力如何？对开放挑战的实证研究 

A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System

Bolin Gao, Keke Wan, Qien Chen, Zhou Wang, Rui Li, Yu Jiang, Run Mei, Yinghui Luo, Keqiang Li

https://link.springer.com/article/10.1007/s11633-022-1395-3

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1395-3

中文导读：

清华李克强院士团队 | 综述：汽车预测性巡航控制及其在云控系统下的典型技术进展与展望

Machine Learning Methods in Solving the Boolean Satisfiability Problem

Wenxuan Guo, Hui-Ling Zhen, Xijun Li, Wanqian Luo, Mingxuan Yuan, Yaohui Jin, Junchi Yan

https://link.springer.com/article/10.1007/s11633-022-1396-2

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1396-2

中文导读：

上海交大严骏驰团队 | 综述: 求解布尔可满足性问题(SAT)的机器学习方法 

YuNet: A Tiny Millisecond-level Face Detector

Wei Wu, Hanyang Peng, Shiqi Yu

https://link.springer.com/article/10.1007/s11633-023-1423-y

https://www.mi-research.net/en/article/doi/10.1007/s11633-023-1423-y

中文导读：

南科大于仕琪团队 | YuNet:一个速度为毫秒级的人脸检测器 

Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients

Cheng-Cheng Ma, Bao-Yuan Wu, Yan-Bo Fan, Yong Zhang, Zhi-Feng Li

https://link.springer.com/article/10.1007/s11633-022-1328-1

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1328-1

FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication

Feng-Zhao Lian, Jun-Duan Huang, Ji-Xin Liu, Guang Chen, Jun-Hong Zhao, Wen-Xiong Kang

https://link.springer.com/article/10.1007/s11633-022-1341-4

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1341-4

ECG Biometrics via Enhanced Correlation and Semantic-rich Embedding

Kui-Kui Wang, Gong-Ping Yang, Lu Yang, Yu-Wen Huang, Yi-Long Yin

https://link.springer.com/article/10.1007/s11633-022-1345-0

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1345-0

Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

Chun-Ying Liu, Gong-Ping Yang, Yu-Wen Huang, Fu-Xian Huang

https://link.springer.com/article/10.1007/s11633-022-1366-8

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1366-8

YOLO-CORE: Contour Regression for Efficient Instance Segmentation

Haoliang Liu, Wei Xiong, Yu Zhang

https://link.springer.com/article/10.1007/s11633-022-1379-3

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1379-3

Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

Yu Xi, Ke Zhou, Ling-Wen Meng, Bo Chen, Hao-Min Chen, Jing-Yi Zhang

https://link.springer.com/article/10.1007/s11633-022-1355-y

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1355-y

Biological Eagle-eye Inspired Target Detection for Unmanned Aerial Vehicles Equipped with a Manipulator

Yi-Min Deng, Si-Yuan Wang

https://link.springer.com/article/10.1007/s11633-022-1342-3

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1342-3

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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。