  Multimodal video understanding is crucial for analyzing egocentric videos,
where integrating multiple sensory signals significantly enhances action
recognition and moment localization. However, practical applications often
grapple with incomplete modalities due to factors like privacy concerns,
efficiency demands, or hardware malfunctions. Addressing this, our study delves
into the impact of missing modalities on egocentric action recognition,
particularly within transformer-based models. We introduce a novel concept
-Missing Modality Token (MMT)-to maintain performance even when modalities are
absent, a strategy that proves effective in the Ego4D, Epic-Kitchens, and
Epic-Sounds datasets. Our method mitigates the performance loss, reducing it
from its original $\sim 30\%$ drop to only $\sim 10\%$ when half of the test
set is modal-incomplete. Through extensive experimentation, we demonstrate the
adaptability of MMT to different training scenarios and its superiority in
handling missing modalities compared to current methods. Our research
contributes a comprehensive analysis and an innovative approach, opening
avenues for more resilient multimodal systems in real-world settings.
