  The exploration of video content via Self-Supervised Learning (SSL) models
has unveiled a dynamic field of study, emphasizing both the complex challenges
and unique opportunities inherent in this area. Despite the growing body of
research, the ability of SSL models to detect physical contacts in videos
remains largely unexplored, particularly the effectiveness of methods such as
downstream supervision with linear probing or full fine-tuning. This work aims
to bridge this gap by employing eight different convolutional neural networks
(CNNs) based video SSL models to identify instances of physical contact within
video sequences specifically. The Something-Something v2 (SSv2) and
Epic-Kitchen (EK-100) datasets were chosen for evaluating these approaches due
to the promising results on UCF101 and HMDB51, coupled with their limited prior
assessment on SSv2 and EK-100. Additionally, these datasets feature diverse
environments and scenarios, essential for testing the robustness and accuracy
of video-based models. This approach not only examines the effectiveness of
each model in recognizing physical contacts but also explores the performance
in the action recognition downstream task. By doing so, valuable insights into
the adaptability of SSL models in interpreting complex, dynamic visual
information are contributed.
