Recognizing Actions in Videos under Domain Shift

Action recognition, which consists in automatically recognizing the action being performed in a video sequence, is a fundamental task in computer vision and multimedia. Supervised action recognition has been widely studied because of the growing need for automatically categorizing video content that are being generated everyday. However, it is nearly impossible for human annotators to keep pace with the enormous volumes of online videos, and thus supervised training becomes infeasible. A cheaper way of leveraging the massive pool of unlabelled data is by exploiting an already trained model to infer the labels on such data and then re-using them to build an improved model. Such an approach is also prone to failure because the unlabelled data may belong to a data distribution that is different from the annotated one. This is often referred to as the domain-shift problem. To address the domain-shift, recently Unsupervised Video Domain Adaptation (UVDA) methods have been proposed. However, these methods typically make strong and unrealistic assumptions. In this talk I will present some recent works of my research group on UVDA, showing that, thanks to recent advances in deep architectures and to the advent of foundation models, it is possible to deal with more challenging and realistic settings and recognize out-of-distribution classes.