  Most methods tackle zero-shot video classification by aligning
visual-semantic representations within seen classes, which limits
generalization to unseen classes. To enhance model generalizability, this paper
presents an end-to-end framework that preserves alignment and uniformity
properties for representations on both seen and unseen classes. Specifically,
we formulate a supervised contrastive loss to simultaneously align
visual-semantic features (i.e., alignment) and encourage the learned features
to distribute uniformly (i.e., uniformity). Unlike existing methods that only
consider the alignment, we propose uniformity to preserve maximal-info of
existing features, which improves the probability that unobserved features fall
around observed data. Further, we synthesize features of unseen classes by
proposing a class generator that interpolates and extrapolates the features of
seen classes. Besides, we introduce two metrics, closeness and dispersion, to
quantify the two properties and serve as new measurements of model
generalizability. Experiments show that our method significantly outperforms
SoTA by relative improvements of 28.1% on UCF101 and 27.0% on HMDB51. Code is
available.
