  While progress has been made in the domain of video-language understanding,
current state-of-the-art algorithms are still limited in their ability to
understand videos at high levels of abstraction, such as news-oriented videos.
Alternatively, humans easily amalgamate information from video and language to
infer information beyond what is visually observable in the pixels. An example
of this is watching a news story, where the context of the event can play as
big of a role in understanding the story as the event itself. Towards a
solution for designing this ability in algorithms, we present a large-scale
analysis on an in-house dataset collected by the Reuters News Agency, called
Reuters Video-Language News (ReutersViLNews) dataset which focuses on
high-level video-language understanding with an emphasis on long-form news. The
ReutersViLNews Dataset consists of long-form news videos collected and labeled
by news industry professionals over several years and contains prominent news
reporting from around the world. Each video involves a single story and
contains action shots of the actual event, interviews with people associated
with the event, footage from nearby areas, and more. ReutersViLNews dataset
contains videos from seven subject categories: disaster, finance,
entertainment, health, politics, sports, and miscellaneous with annotations
from high-level to low-level, title caption, visual video description,
high-level story description, keywords, and location. We first present an
analysis of the dataset statistics of ReutersViLNews compared to previous
datasets. Then we benchmark state-of-the-art approaches for four different
video-language tasks. The results suggest that news-oriented videos are a
substantial challenge for current video-language understanding algorithms and
we conclude by providing future directions in designing approaches to solve the
ReutersViLNews dataset.
