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

In this work, we study the effect of occlusion on video action recognition.

Existing work has attempted to alleviate these problems by blurring faces, downsampling videos, or training on synthetic data. On the other hand, analysis on the transferability of privacy-preserving pre-trained models to downstream tasks has been limited.

In this case, accurately recognizing the action requires richer contextual information from the background and surroundings.

However, existing video classification models rely on the i.i.d.

The challenge of replicating research results has posed a significant impediment to the field of molecular biology. The advent of modern intelligent systems has led to notable progress in various domains.

Human actions in egocentric videos often feature hand-object interactions composed of a verb (performed by the hand) applied to an object.

Compressed videos offer a compelling alternative to raw videos, showing the possibility to significantly reduce the on-line computational and storage cost.