RL-SSI Model: Adapting a Supervised Learning Approach to a Semi-Supervised Approach for Human Action Recognition

RL-SSI Model: Adapting a Supervised Learning Approach to a Semi-Supervised Approach for Human Action Recognition

Generally, the action recognition task requires a vast amount of labeled data, which represents a time-consuming human annotation effort. To mitigate the dependency on labeled data, this study proposes Semi-Supervised and Iterative Reinforcement Learning (RL-SSI), which adapts a supervised approach...

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Bibliographic Details
Main Authors: Lucas Lisboa dos Santos, Ingrid Winkler, Erick Giovani Sperandio Nascimento
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Electronics
Subjects:
deep learning
human action recognition
data annotation
semi-supervised learning
action segmentation
Online Access:https://www.mdpi.com/2079-9292/11/9/1471

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