  This paper presents a framework for the analysis of changes in visual
streams: ordered sequences of images, possibly separated by significant time
gaps. We propose a new approach to incorporating unlabeled data into training
to generate natural language descriptions of change. We also develop a
framework for estimating the time of change in visual stream. We use learned
representations for change evidence and consistency of perceived change, and
combine these in a regularized graph cut based change detector. Experimental
evaluation on visual stream datasets, which we release as part of our
contribution, shows that representation learning driven by natural language
descriptions significantly improves change detection accuracy, compared to
methods that do not rely on language.
