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

Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach