MTL vs STL: NIR Video metadata classification using self-supervised semi-supervised learning

Abstract

Abstract The combination of self-supervision and semi-supervision has emerged as a popular research topic in recent years. However, existing studies primarily focus on single-task models trained on datasets where individual images are labeled with a single class, overlooking the challenges associated with multi-class scenarios. In this thesis, we propose a modified S4L framework, which is a self-supervised semi supervised learning framework specifically designed to handle partially labeled data. The modified S4L framework addresses some limitations of previous approaches and demonstrates its effectiveness in both multi-task learning and single-task learning settings. The research focuses on the classification of visual attributes in human subjects, specifically in near-infrared (NIR) images.