Learning Using Privileged Information for Zero-Shot Action Recognition. (arXiv:2206.08632v2 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2206.08632

Zero-Shot Action Recognition (ZSAR) aims to recognize video actions that have
never been seen during training. Most existing methods assume a shared semantic
space between seen and unseen actions and intend to directly learn a mapping
from a visual space to the semantic space. This approach has been challenged by
the semantic gap between the visual space and semantic space. This paper
presents a novel method that uses object semantics as privileged information to
narrow the semantic gap and, hence, …

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