  Generic event boundary detection is an important yet challenging task in
video understanding, which aims at detecting the moments where humans naturally
perceive event boundaries. The main challenge of this task is perceiving
various temporal variations of diverse event boundaries. To this end, this
paper presents an effective and end-to-end learnable framework (DDM-Net). To
tackle the diversity and complicated semantics of event boundaries, we make
three notable improvements. First, we construct a feature bank to store
multi-level features of space and time, prepared for difference calculation at
multiple scales. Second, to alleviate inadequate temporal modeling of previous
methods, we present dense difference maps (DDM) to comprehensively characterize
the motion pattern. Finally, we exploit progressive attention on multi-level
DDM to jointly aggregate appearance and motion clues. As a result, DDM-Net
respectively achieves a significant boost of 14% and 8% on Kinetics-GEBD and
TAPOS benchmark, and outperforms the top-1 winner solution of LOVEU
Challenge@CVPR 2021 without bells and whistles. The state-of-the-art result
demonstrates the effectiveness of richer motion representation and more
sophisticated aggregation, in handling the diversity of generic event boundary
detection. The code is made available at \url{https://github.com/MCG-NJU/DDM}.
