  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
contains significant spatio-temporal redundancy and demands considerable
computational power and storage space. To remedy these issues, we propose a
novel compressed video representation learning method for event boundary
detection that is fully end-to-end leveraging 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
use lightweight ConvNets to extract features of the P-frames in the GOPs and
spatial-channel attention module (SCAM) is designed to refine the feature
representations of the P-frames based on the compressed information with
bidirectional information flow. To learn a suitable representation for boundary
detection, we construct the local frames bag for each candidate frame and use
the long short-term memory (LSTM) module to capture temporal relationships. We
then compute frame differences with group similarities in the temporal domain.
This module is only applied within a local window, which is critical for event
boundary detection. Finally a simple classifier is used to determine the event
boundaries of video sequences based on the learned feature representation. 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 and TAPOS datasets demonstrate that
the proposed method achieves considerable improvements compared to previous
end-to-end approach while running at the same speed. The code is available at
https://github.com/GX77/LCVSL.
