  With the advent of media streaming, video action recognition has become
progressively important for various applications, yet at the high expense of
requiring large-scale data labelling. To overcome the problem of expensive data
labelling, domain adaptation techniques have been proposed that transfers
knowledge from fully labelled data (i.e., source domain) to unlabelled data
(i.e., target domain). The majority of video domain adaptation algorithms are
proposed for closed-set scenarios in which all the classes are shared among the
domains. In this work, we propose an open-set video domain adaptation approach
to mitigate the domain discrepancy between the source and target data, allowing
the target data to contain additional classes that do not belong to the source
domain. Different from previous works, which only focus on improving accuracy
for shared classes, we aim to jointly enhance the alignment of shared classes
and recognition of unknown samples. Towards this goal, class-conditional
extreme value theory is applied to enhance the unknown recognition.
Specifically, the entropy values of target samples are modelled as generalised
extreme value distributions, which allows separating unknown samples lying in
the tail of the distribution. To alleviate the negative transfer issue, weights
computed by the distance from the sample entropy to the threshold are leveraged
in adversarial learning in the sense that confident source and target samples
are aligned, and unconfident samples are pushed away. The proposed method has
been thoroughly evaluated on both small-scale and large-scale cross-domain
video datasets and achieved the state-of-the-art performance.
