Temporal video segmentation and classification have been advanced greatly by
public benchmarks in recent years. However, such research still mainly focuses
on human actions, failing to describe videos in a holistic view. In addition,
previous research tends to pay much attention to visual information yet ignores
the multi-modal nature of videos. To fill this gap, we construct the Tencent
`Ads Video Segmentation'~(TAVS) dataset in the ads domain to escalate
multi-modal video analysis to a new level. TAVS describes videos from three
independent perspectives as `presentation form', `place', and `style', and
contains rich multi-modal information such as video, audio, and text. TAVS is
organized hierarchically in semantic aspects for comprehensive temporal video
segmentation with three levels of categories for multi-label classification,
e.g., `place' - `working place' - `office'. Therefore, TAVS is distinguished
from previous temporal segmentation datasets due to its multi-modal
information, holistic view of categories, and hierarchical granularities. It
includes 12,000 videos, 82 classes, 33,900 segments, 121,100 shots, and 168,500
labels. Accompanied with TAVS, we also present a strong multi-modal video
segmentation baseline coupled with multi-label class prediction. Extensive
experiments are conducted to evaluate our proposed method as well as existing
representative methods to reveal key challenges of our dataset TAVS