Generic event boundary detection aims to localize the generic, taxonomy-free
event boundaries that segment videos into chunks. Existing methods typically
require video frames to be decoded before feeding into the network, which
demands considerable computational power and storage space. To that end, we
propose a new end-to-end compressed video representation learning for event
boundary detection that leverages the rich information in the compressed
domain, i.e., RGB, motion vectors, residuals, and the internal group of
pictures (GOP) structure, without fully decoding the video. Specifically, we
first use the ConvNets to extract features of the I-frames in the GOPs. After
that, a light-weight spatial-channel compressed encoder is designed to compute
the feature representations of the P-frames based on the motion vectors,
residuals and representations of their dependent I-frames. A temporal
contrastive module is proposed to determine the event boundaries of video
sequences. To remedy the ambiguities of annotations and speed up the training
process, we use the Gaussian kernel to preprocess the ground-truth event
boundaries. Extensive experiments conducted on the Kinetics-GEBD dataset
demonstrate that the proposed method achieves comparable results to the
state-of-the-art methods with $4.5\times$ faster running speed.

该研究提出了一种基于压缩视频表示学习的端到端事件边界检测新方法，该方法利用压缩域中的丰富信息进行特征提取和边界检测，并在 Kinetics-GEBD 数据集上进行了广泛的实验，取得了与最先进方法相当的结果，并具有更快的运行速度。