Deep learning models perform remarkably well for the same task under the
assumption that data is always coming from the same distribution.
However, this is generally violated in practice, mainly due to the
differences in the data acquisition techniques and the lack of
information about the underlying source of new data. Domain
Generalization targets the ability to generalize to test data of an
unseen domain; while this problem is well-studied for images, such
studies are significantly lacking in spatiotemporal visual content –
videos and GIFs. This is due to (1) the challenging nature of
misalignment of temporal features and the varying appearance/motion of
actors and actions in different domains, and (2) spatiotemporal datasets
being laborious to collect and annotate for multiple domains. We collect
and present the first synthetic video dataset of Animated GIFs for
domain generalization, Ani-GIFs, that is used to study domain gap
of videos vs. GIFs, and animated vs. real GIFs, for the
task of action recognition. We provide a training and testing setting
for Ani-GIFs, and extend two domain generalization baseline
approaches, based on data augmentation and explainability, to the
spatiotemporal domain to catalyze research in this direction.
Ani-GIFs: A Benchmark Dataset for Domain Generalization of Action Recognition from GIFs