A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data

TL;DR

This survey reviews machine learning techniques aimed at reducing data labeling costs for video, audio, and text, emphasizing auto annotation methods to improve efficiency and model robustness.

Contribution

It provides a comprehensive overview of existing auto labeling techniques across multiple data modalities, highlighting recent advances and research trends.

Findings

Transfer learning reduces data annotation needs.

Auto labeling techniques improve efficiency in data preparation.

Research focus is shifting towards automated annotation methods.

Abstract

Machine learning has been utilized to perform tasks in many different domains such as classification, object detection, image segmentation and natural language analysis. Data labeling has always been one of the most important tasks in machine learning. However, labeling large amounts of data increases the monetary cost in machine learning. As a result, researchers started to focus on reducing data annotation and labeling costs. Transfer learning was designed and widely used as an efficient approach that can reasonably reduce the negative impact of limited data, which in turn, reduces the data preparation cost. Even transferring previous knowledge from a source domain reduces the amount of data needed in a target domain. However, large amounts of annotated data are still demanded to build robust models and improve the prediction accuracy of the model. Therefore, researchers started to pay more attention on auto annotation and labeling. In this survey paper, we provide a review of previous techniques that focuses on optimized data annotation and labeling for video, audio, and text data.