  Recently, self-supervised learning has proved to be effective to learn
representations of events suitable for temporal segmentation in image
sequences, where events are understood as sets of temporally adjacent images
that are semantically perceived as a whole. However, although this approach
does not require expensive manual annotations, it is data hungry and suffers
from domain adaptation problems. As an alternative, in this work, we propose a
novel approach for learning event representations named Dynamic Graph Embedding
(DGE). The assumption underlying our model is that a sequence of images can be
represented by a graph that encodes both semantic and temporal similarity. The
key novelty of DGE is to learn jointly the graph and its graph embedding. At
its core, DGE works by iterating over two steps: 1) updating the graph
representing the semantic and temporal similarity of the data based on the
current data representation, and 2) updating the data representation to take
into account the current data graph structure. The main advantage of DGE over
state-of-the-art self-supervised approaches is that it does not require any
training set, but instead learns iteratively from the data itself a
low-dimensional embedding that reflects their temporal and semantic similarity.
Experimental results on two benchmark datasets of real image sequences captured
at regular time intervals demonstrate that the proposed DGE leads to event
representations effective for temporal segmentation. In particular, it achieves
robust temporal segmentation on the EDUBSeg and EDUBSeg-Desc benchmark
datasets, outperforming the state of the art. Additional experiments on two
Human Motion Segmentation benchmark datasets demonstrate the generalization
capabilities of the proposed DGE.
