  This technical report describes the SViT approach for the Ego4D Point of No
Return (PNR) Temporal Localization Challenge. We propose a learning framework
StructureViT (SViT for short), which demonstrates how utilizing the structure
of a small number of images only available during training can improve a video
model. SViT relies on two key insights. First, as both images and videos
contain structured information, we enrich a transformer model with a set of
\emph{object tokens} that can be used across images and videos. Second, the
scene representations of individual frames in video should "align" with those
of still images. This is achieved via a "Frame-Clip Consistency" loss, which
ensures the flow of structured information between images and videos. SViT
obtains strong performance on the challenge test set with 0.656 absolute
temporal localization error.
