Deep Learning in AI for Information Fusion Panel Discussion

被引：3

作者：

Blasch, Erik ^{[1
]}

Kadar, Ivan ^{[2
]}

Grewe, Lynne L. ^{[3
]}

Stevenson, Garrett ^{[3
]}

Majumder, Uttam K. ^{[4
]}

Chong, Chee-Yee ^{[5
]}

机构：

[1] US Air Force, AFOSR, Res Lab, Arlington, VA 22203 USA

[2] Interlink Syst Sci Inc, 1979 Marcus Ave, Lake Success, NY 11042 USA

[3] Calif State Univ, East Bay 25800 Carlos Bee Blvd, Hayward, CA 94542 USA

[4] US Air Force, Res Lab, Informat Directorate, Rome, NY 13441 USA

[5] POB 4082, Los Altos, CA 94024 USA

来源：

SIGNAL PROCESSING, SENSOR/INFORMATION FUSION, AND TARGET RECOGNITION XXVIII | 2019年 / 11018卷

关键词：

Artificial Intelligence; Multimodal Deep Learning; Deep Neural Networks; Context-enhanced information; fusion; situation assessment; probabilistic models; target-tracking and recognition; temporal networks;

D O I：

10.1117/12.2519230

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

During the 2018 SPIE DSS conference, panelists were invited to highlight the trends and use of artificial intelligence and deep learning (AI/DL) for information fusion. This paper highlights the common issues presented from the panel discussion. The key issues include: leveraging AI/DL coordinated with information fusion for: ( 1) knowledge reasoning and reasoning, (2) information fusion enhancement, (3) object recognition and tracking, (4) data with models fusion, and (5) deep multimodal fusion cognition strategies to support the user.

引用

页数：13