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

共 50 条

[21] InSAR Phase Unwrapping by Deep Learning Based on Gradient Information Fusion
Li, Liutong
Zhang, Hong
Tang, Yixian
Wang, Chao
Gu, Feng
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
[22] GlobalFusion: A Global Attentional Deep Learning Framework for Multisensor Information Fusion
Liu, Shengzhong
Yao, Shuochao
Li, Jinyang
Liu, Dongxin
Wang, Tianshi
Shao, Huajie
Abdelzaher, Tarek
PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT, 2020, 4 (01):
[23] Fusion of tactile and visual information in deep learning models for object recognition
Babadian, Reza Pebdani
Faez, Karim
Amiri, Mahmood
Falotico, Egidio
INFORMATION FUSION, 2023, 92 : 313 - 325
[24] Panel: Using Generative AI in Teaching and Learning
Sumner, Mary
Van Slyke, Craig
Galletta, Dennis F.
Niederman, Fred
PROCEEDINGS OF THE 2024 COMPUTERS AND PEOPLE RESEARCH CONFERENCE, SIGMIS-CPR 2024, 2024,
[25] WHAT ARE THE PESTICIDE INFORMATION PROBLEMS - A PANEL DISCUSSION
MAGEE, RJ
JOURNAL OF CHEMICAL DOCUMENTATION, 1964, 4 (03): : 164 - 164
[26] The discussion of information fusion technique of fuze
Wang, X
Zhang, H
ISTM/2003: 5TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6, CONFERENCE PROCEEDINGS, 2003, : 1115 - 1118
[27] Deep Learning and Lung Cancer: AI to Extract Information Hidden in Routine CT Scans
Shaffer, Kitt
RADIOLOGY, 2020, 296 (01) : 225 - 226
[28] Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion
Kokel, Harsha
Prabhakar, Nikhilesh
Ravindran, Balaraman
Blasch, Erik
Tadepalli, Prasad
Natarajan, Sriraam
2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022), 2022,
[29] Multichannel Information Fusion and Deep Transfer Learning for Rotating Machinery Fault Diagnosis
Zhang L.
Hu Y.
Zhao L.
Zhang H.
Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2023, 34 (08): : 966 - 975
[30] Artificial Intelligence Algorithms for Multisensor Information Fusion Based on Deep Learning Algorithms
Jiang, Lan
MOBILE INFORMATION SYSTEMS, 2022, 2022

← 1 2 3 4 5 →