  Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of
adapting an action recognition model from a labelled source domain to an
unlabelled target domain that contains "target-private" categories, which are
present in the target but absent in the source. In this work we deviate from
the prior work of training a specialized open-set classifier or weighted
adversarial learning by proposing to use pre-trained Language and Vision Models
(CLIP). The CLIP is well suited for OUVDA due to its rich representation and
the zero-shot recognition capabilities. However, rejecting target-private
instances with the CLIP's zero-shot protocol requires oracle knowledge about
the target-private label names. To circumvent the impossibility of the
knowledge of label names, we propose AutoLabel that automatically discovers and
generates object-centric compositional candidate target-private class names.
Despite its simplicity, we show that CLIP when equipped with AutoLabel can
satisfactorily reject the target-private instances, thereby facilitating better
alignment between the shared classes of the two domains. The code is available.
