  As one of the fundamental video tasks in computer vision, Open-Vocabulary
Action Recognition (OVAR) recently gains increasing attention, with the
development of vision-language pre-trainings. To enable generalization of
arbitrary classes, existing methods treat class labels as text descriptions,
then formulate OVAR as evaluating embedding similarity between visual samples
and textual classes. However, one crucial issue is completely ignored: the
class descriptions given by users may be noisy, e.g., misspellings and typos,
limiting the real-world practicality of vanilla OVAR. To fill the research gap,
this paper pioneers to evaluate existing methods by simulating multi-level
noises of various types, and reveals their poor robustness. To tackle the noisy
OVAR task, we further propose one novel DENOISER framework, covering two parts:
generation and discrimination. Concretely, the generative part denoises noisy
class-text names via one decoding process, i.e., propose text candidates, then
utilize inter-modal and intra-modal information to vote for the best. At the
discriminative part, we use vanilla OVAR models to assign visual samples to
class-text names, thus obtaining more semantics. For optimization, we
alternately iterate between generative and discriminative parts for progressive
refinements. The denoised text classes help OVAR models classify visual samples
more accurately; in return, classified visual samples help better denoising. On
three datasets, we carry out extensive experiments to show our superior
robustness, and thorough ablations to dissect the effectiveness of each
component.
