  Video ads segmentation and tagging is a challenging task due to two main
reasons: (1) the video scene structure is complex and (2) it includes multiple
modalities (e.g., visual, audio, text.). While previous work focuses mostly on
activity videos (e.g. "cooking", "sports"), it is not clear how they can be
leveraged to tackle the task of video ads content structuring. In this paper,
we propose a two-stage method that first provides the boundaries of the scenes,
and then combines a confidence score for each segmented scene and the tag
classes predicted for that scene. We provide extensive experimental results on
the network architectures and modalities used for the proposed method. Our
combined method improves the previous baselines on the challenging "Tencent
Advertisement Video" dataset.
