Deep learning models have become central to computer vision, excelling across various tasks when trained on large labeled datasets. However, supervised training has scalability issues since gathering quality labeled data is a costly and time-intensive process. Self-Supervised Learning (SSL) methods offer a viable alternative to this paradigm, as they enable models to learn directly from input data without task-specific labels. This approach produces general representations that can be reused across diverse tasks and domains, including those with limited annotations. This study focuses on egocentric vision, a field aimed at capturing user actions and interactions within the environment from a first-person perspective.