  Cognitive science has shown that humans perceive videos in terms of events
separated by the state changes of dominant subjects. State changes trigger new
events and are one of the most useful among the large amount of redundant
information perceived. However, previous research focuses on the overall
understanding of segments without evaluating the fine-grained status changes
inside. In this paper, we introduce a new dataset called Kinetic-GEB+. The
dataset consists of over 170k boundaries associated with captions describing
status changes in the generic events in 12K videos. Upon this new dataset, we
propose three tasks supporting the development of a more fine-grained, robust,
and human-like understanding of videos through status changes. We evaluate many
representative baselines in our dataset, where we also design a new TPD
(Temporal-based Pairwise Difference) Modeling method for visual difference and
achieve significant performance improvements. Besides, the results show there
are still formidable challenges for current methods in the utilization of
different granularities, representation of visual difference, and the accurate
localization of status changes. Further analysis shows that our dataset can
drive developing more powerful methods to understand status changes and thus
improve video level comprehension. The dataset is available at
https://github.com/showlab/GEB-Plus
