  We address temporal localization of events in large-scale video data, in the
context of the Youtube-8M Segments dataset. This emerging field within video
recognition can enable applications to identify the precise time a specified
event occurs in a video, which has broad implications for video search. To
address this we present two separate approaches: (1) a gradient boosted
decision tree model on a crafted dataset and (2) a combination of deep learning
models based on frame-level data, video-level data, and a localization model.
The combinations of these two approaches achieved 5th place in the 3rd
Youtube-8M video recognition challenge.
