  Generic event boundary detection (GEBD), inspired by human visual cognitive
behaviors of consistently segmenting videos into meaningful temporal chunks,
finds utility in various applications such as video editing and. In this paper,
we demonstrate that SOTA GEBD models often prioritize final performance over
model complexity, resulting in low inference speed and hindering efficient
deployment in real-world scenarios. We contribute to addressing this challenge
by experimentally reexamining the architecture of GEBD models and uncovering
several surprising findings. Firstly, we reveal that a concise GEBD baseline
model already achieves promising performance without any sophisticated design.
Secondly, we find that the widely applied image-domain backbones in GEBD models
can contain plenty of architecture redundancy, motivating us to gradually
``modernize'' each component to enhance efficiency. Thirdly, we show that the
GEBD models using image-domain backbones conducting the spatiotemporal learning
in a spatial-then-temporal greedy manner can suffer from a distraction issue,
which might be the inefficient villain for GEBD. Using a video-domain backbone
to jointly conduct spatiotemporal modeling is an effective solution for this
issue. The outcome of our exploration is a family of GEBD models, named
EfficientGEBD, significantly outperforms the previous SOTA methods by up to
1.7\% performance gain and 280\% speedup under the same backbone. Our research
prompts the community to design modern GEBD methods with the consideration of
model complexity, particularly in resource-aware applications. The code is
available at \url{https://github.com/Ziwei-Zheng/EfficientGEBD}.
