  Generic event boundary detection (GEBD) aims at pinpointing event boundaries
naturally perceived by humans, playing a crucial role in understanding
long-form videos. Given the diverse nature of generic boundaries, spanning
different video appearances, objects, and actions, this task remains
challenging. Existing methods usually detect various boundaries by the same
protocol, regardless of their distinctive characteristics and detection
difficulties, resulting in suboptimal performance. Intuitively, a more
intelligent and reasonable way is to adaptively detect boundaries by
considering their special properties. In light of this, we propose a novel
dynamic pipeline for generic event boundaries named DyBDet. By introducing a
multi-exit network architecture, DyBDet automatically learns the subnet
allocation to different video snippets, enabling fine-grained detection for
various boundaries. Besides, a multi-order difference detector is also proposed
to ensure generic boundaries can be effectively identified and adaptively
processed. Extensive experiments on the challenging Kinetics-GEBD and TAPOS
datasets demonstrate that adopting the dynamic strategy significantly benefits
GEBD tasks, leading to obvious improvements in both performance and efficiency
compared to the current state-of-the-art.
