Our preliminary experiments have shown that the effectiveness of the existing methods often drastically decrease or even fail when tackling lip-syncing videos.

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

While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexpected situations.

When releasing outputs from confidential data, agencies need to balance the analytical usefulness of the released data with the obligation to protect data subjects'