  We propose a framework for early action recognition and anticipation by
correlating past features with the future using three novel similarity measures
called Jaccard vector similarity, Jaccard cross-correlation and Jaccard
Frobenius inner product over covariances. Using these combinations of novel
losses and using our framework, we obtain state-of-the-art results for early
action recognition in UCF101 and JHMDB datasets by obtaining 91.7 % and 83.5 %
accuracy respectively for an observation percentage of 20. Similarly, we obtain
state-of-the-art results for Epic-Kitchen55 and Breakfast datasets for action
anticipation by obtaining 20.35 and 41.8 top-1 accuracy respectively.
