Recognizing human actions in videos requires spatial and temporal understanding. Most existing action recognition models lack a balanced spatio-temporal understanding of videos.

Existing regularization methods merely minimize the norms of embeddings to regularize the model, leading to suboptimal performance.

The biases ofSnow are manifested in the statistical properties of itsperspectives(i.e.