Attributes’ Importance for Zero-Shot Pose-Classification Based on Wearable Sensors

Attributes’ Importance for Zero-Shot Pose-Classification Based on Wearable Sensors

This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with all the avai...

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Bibliographic Details
Main Authors: Hiroki Ohashi, Mohammad Al-Naser, Sheraz Ahmed, Katsuyuki Nakamura, Takuto Sato, Andreas Dengel
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Sensors
Subjects:
zero-shot learning
wearable sensor
IMU
pose classification
action recognition
time-series
CNN
Online Access:http://www.mdpi.com/1424-8220/18/8/2485

Internet

http://www.mdpi.com/1424-8220/18/8/2485

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