  The task of Generic Event Boundary Detection (GEBD) aims to detect moments in
videos that are naturally perceived by humans as generic and taxonomy-free
event boundaries. Modeling the dynamically evolving temporal and spatial
changes in a video makes GEBD a difficult problem to solve. Existing approaches
involve very complex and sophisticated pipelines in terms of architectural
design choices, hence creating a need for more straightforward and simplified
approaches. In this work, we address this issue by revisiting a simple and
effective self-supervised method and augment it with a differentiable motion
feature learning module to tackle the spatial and temporal diversities in the
GEBD task. We perform extensive experiments on the challenging Kinetics-GEBD
and TAPOS datasets to demonstrate the efficacy of the proposed approach
compared to the other self-supervised state-of-the-art methods. We also show
that this simple self-supervised approach learns motion features without any
explicit motion-specific pretext task.
