  Micro-videos are six-second videos popular on social media networks with
several unique properties. Firstly, because of the authoring process, they
contain significantly more diversity and narrative structure than existing
collections of video "snippets". Secondly, because they are often captured by
hand-held mobile cameras, they contain specialized viewpoints including
third-person, egocentric, and self-facing views seldom seen in traditional
produced video. Thirdly, due to to their continuous production and publication
on social networks, aggregate micro-video content contains interesting
open-world dynamics that reflects the temporal evolution of tag topics. These
aspects make micro-videos an appealing well of visual data for developing
large-scale models for video understanding. We analyze a novel dataset of
micro-videos labeled with 58 thousand tags. To analyze this data, we introduce
viewpoint-specific and temporally-evolving models for video understanding,
defined over state-of-the-art motion and deep visual features. We conclude that
our dataset opens up new research opportunities for large-scale video analysis,
novel viewpoints, and open-world dynamics.
