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...
Main Authors: | Reina Ishikawa, Ryo Hachiuma, Hideo Saito |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9416486/ |
Similar Items
-
Audio-Visual Self-Supervised Terrain Type Recognition for Ground Mobile Platforms
by: Akiyoshi Kurobe, et al.
Published: (2021-01-01) -
Semantic Consistency Cross-Modal Retrieval With Semi-Supervised Graph Regularization
by: Gongwen Xu, et al.
Published: (2020-01-01) -
Adaptive Regularized Semi-Supervised Clustering Ensemble
by: Rui Luo, et al.
Published: (2020-01-01) -
Clustering versus Incremental Learning Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification
by: Muhammad Anwar Ma’sum, et al.
Published: (2020-01-01) -
Using Semi-supervised Clustering for Neurons Classification
by: Fakhraee Seyedabad, Ali
Published: (2013)