  We investigate video transforms that result in class-homogeneous
label-transforms. These are video transforms that consistently maintain or
modify the labels of all videos in each class. We propose a general approach to
discover invariant classes, whose transformed examples maintain their label;
pairs of equivariant classes, whose transformed examples exchange their labels;
and novel-generating classes, whose transformed examples belong to a new class
outside the dataset. Label transforms offer additional supervision previously
unexplored in video recognition benefiting data augmentation and enabling
zero-shot learning opportunities by learning a class from transformed videos of
its counterpart.
  Amongst such video transforms, we study horizontal-flipping, time-reversal,
and their composition. We highlight errors in naively using horizontal-flipping
as a form of data augmentation in video. Next, we validate the realism of
time-reversed videos through a human perception study where people exhibit
equal preference for forward and time-reversed videos. Finally, we test our
approach on two datasets, Jester and Something-Something, evaluating the three
video transforms for zero-shot learning and data augmentation. Our results show
that gestures such as zooming in can be learnt from zooming out in a zero-shot
setting, as well as more complex actions with state transitions such as digging
something out of something from burying something in something.
