  This paper presents our approach to the third YouTube-8M video understanding
competition that challenges par-ticipants to localize video-level labels at
scale to the pre-cise time in the video where the label actually occurs.
Ourmodel is an ensemble of frame-level models such as GatedNetVLAD and NeXtVLAD
and various BERT models withtest-time augmentation. We explore multiple ways to
ag-gregate BERT outputs as video representation and variousways to combine
visual and audio information. We proposetest-time augmentation as shifting
video frames to one leftor right unit, which adds variety to the predictions
and em-pirically shows improvement in evaluation metrics. We firstpre-train the
model on the 4M training video-level data, andthen fine-tune the model on 237K
annotated video segment-level data. We achieve MAP@100K 0.7871 on private
test-ing video segment data, which is ranked 9th over 283 teams.
