  Some cognitive research has discovered that humans accomplish event
segmentation as a side effect of event anticipation. Inspired by this
discovery, we propose a simple yet effective end-to-end self-supervised
learning framework for event segmentation/boundary detection. Unlike the
mainstream clustering-based methods, our framework exploits a transformer-based
feature reconstruction scheme to detect event boundary by reconstruction
errors. This is consistent with the fact that humans spot new events by
leveraging the deviation between their prediction and what is actually
perceived. Thanks to their heterogeneity in semantics, the frames at boundaries
are difficult to be reconstructed (generally with large reconstruction errors),
which is favorable for event boundary detection. Additionally, since the
reconstruction occurs on the semantic feature level instead of pixel level, we
develop a temporal contrastive feature embedding module to learn the semantic
visual representation for frame feature reconstruction. This procedure is like
humans building up experiences with "long-term memory". The goal of our work is
to segment generic events rather than localize some specific ones. We focus on
achieving accurate event boundaries. As a result, we adopt F1 score
(Precision/Recall) as our primary evaluation metric for a fair comparison with
previous approaches. Meanwhile, we also calculate the conventional frame-based
MoF and IoU metric. We thoroughly benchmark our work on four publicly available
datasets and demonstrate much better results.
