  Exploring open-vocabulary video action recognition is a promising venture,
which aims to recognize previously unseen actions within any arbitrary set of
categories. Existing methods typically adapt pretrained image-text models to
the video domain, capitalizing on their inherent strengths in generalization. A
common thread among such methods is the augmentation of visual embeddings with
temporal information to improve the recognition of seen actions. Yet, they
compromise with standard less-informative action descriptions, thus faltering
when confronted with novel actions. Drawing inspiration from human cognitive
processes, we argue that augmenting text embeddings with human prior knowledge
is pivotal for open-vocabulary video action recognition. To realize this, we
innovatively blend video models with Large Language Models (LLMs) to devise
Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to
produce a set of descriptive sentences that contain distinctive features for
identifying given actions. Building upon this foundation, we further introduce
a multi-modal action knowledge alignment mechanism to align concepts in video
and textual knowledge encapsulated within the prompts. Extensive experiments on
various video benchmarks, including zero-shot, few-shot, and base-to-novel
generalization settings, demonstrate that our method not only sets new SOTA
performance but also possesses excellent interpretability.
