Semantic Self Supervision

Semantic self-supervision leverages inherent semantic relationships within data to train models without explicit labels, aiming to improve generalization and data efficiency in various tasks. Current research focuses on integrating vision-language models, contrastive learning, and hybrid matching modules to effectively utilize semantic information from diverse sources like text descriptions and automatically generated labels. This approach shows promise in enhancing performance across diverse applications, including image classification, object detection, and zero-shot learning, particularly in scenarios with limited labeled data or a large number of classes.