Self-Supervised Audio-Visual Feature Learning for Single-modal Incremental Terrain Type Clustering

The key to an accurate understanding of terrain is to extract the informative features from the multi-modal data obtained from different devices. Sensors, such as RGB cameras, depth sensors, vibration sensors, and microphones, are used as the multi-modal data. Many studies have explored ways to use...

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Bibliographic Details
Published in:	IEEE Access Vol. 9; p. 1
Main Authors:	Ishikawa, Reina, Hachiuma, Ryo, Saito, Hideo
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.01.2021 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Ablation Algorithms Audio data Cameras Clustering Data mining Electrical engineering. Electronics. Nuclear engineering Electronic devices Feature extraction Machine learning Microphones Modal data Multi-modal learning Probabilistic models Robotics Robots Self-supervised Sensors Terrain Terrain type clustering Testing TK1-9971 Training Visualization
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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