  Generic event boundary detection (GEBD) is an important yet challenging task
in video understanding, which aims at detecting the moments where humans
naturally perceive event boundaries. In this paper, we present a local context
modeling and global boundary decoding approach for GEBD task. Local context
modeling sub-network is proposed to perceive diverse patterns of generic event
boundaries, and it generates powerful video representations and reliable
boundary confidence. Based on them, global boundary decoding sub-network is
exploited to decode event boundaries from a global view. Our proposed method
achieves 85.13% F1-score on Kinetics-GEBD testing set, which achieves a more
than 22% F1-score boost compared to the baseline method. The code is available
at https://github.com/JackyTown/GEBD_Challenge_CVPR2022.
