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Discovering physics and design trends from visual temporal structures

Living in a constantly changing world, we cannot
help but notice the temporal regularities of visual changes around
us. These changes can be irreversible governed by physical laws,
such as glass bottles broken into pieces, or influenced by design
trends, such as web pages adopting templates with larger background
images. In this dissertation, we build computational models to
discover and apply the knowledge of the physics for arrow of time,
and the design trends for web pages from image sequences. In the
first part of the thesis, I train models to learn the visual cues
that are indicative of the arrow of time from large real world
video datasets. In the second part of the thesis, I investigate the
evolution of visual cues and layout in web page design through
screenshots over time. The knowledge of these visual temporal
structures are not only of scientific interest by themselves, but
also of practical uses demonstrated in this thesis.