  There is a growing trend in placing video advertisements on social platforms
for online marketing, which demands automatic approaches to understand the
contents of advertisements effectively. Taking the 2021 TAAC competition as an
opportunity, we developed a multimodal system to improve the ability of
structured analysis of advertising video content. In our framework, we break
down the video structuring analysis problem into two tasks, i.e., scene
segmentation and multi-modal tagging. In scene segmentation, we build upon a
temporal convolution module for temporal modeling to predict whether adjacent
frames belong to the same scene. In multi-modal tagging, we first compute
clip-level visual features by aggregating frame-level features with
NeXt-SoftDBoF. The visual features are further complemented with textual
features that are derived using a global-local attention mechanism to extract
useful information from OCR (Optical Character Recognition) and ASR (Audio
Speech Recognition) outputs. Our solution achieved a score of 0.2470 measured
in consideration of localization and prediction accuracy, ranking fourth in the
2021 TAAC final leaderboard.
