  Understanding videos that contain multiple modalities is crucial, especially
in egocentric videos, where combining various sensory inputs significantly
improves tasks like action recognition and moment localization. However,
real-world applications often face challenges with incomplete modalities due to
privacy concerns, efficiency needs, or hardware issues. Current methods, while
effective, often necessitate retraining the model entirely to handle missing
modalities, making them computationally intensive, particularly with large
training datasets. In this study, we propose a novel approach to address this
issue at test time without requiring retraining. We frame the problem as a
test-time adaptation task, where the model adjusts to the available unlabeled
data at test time. Our method, MiDl~(Mutual information with
self-Distillation), encourages the model to be insensitive to the specific
modality source present during testing by minimizing the mutual information
between the prediction and the available modality. Additionally, we incorporate
self-distillation to maintain the model's original performance when both
modalities are available. MiDl represents the first self-supervised, online
solution for handling missing modalities exclusively at test time. Through
experiments with various pretrained models and datasets, MiDl demonstrates
substantial performance improvement without the need for retraining.
