  Affective Computing has recently attracted the attention of the research
community, due to its numerous applications in diverse areas. In this context,
the emergence of video-based data allows to enrich the widely used spatial
features with the inclusion of temporal information. However, such
spatio-temporal modelling often results in very high-dimensional feature spaces
and large volumes of data, making training difficult and time consuming. This
paper addresses these shortcomings by proposing a novel model that efficiently
extracts both spatial and temporal features of the data by means of its
enhanced temporal modelling based on latent features. Our proposed model
consists of three major networks, coined Generator, Discriminator, and
Combiner, which are trained in an adversarial setting combined with curriculum
learning to enable our adaptive attention modules. In our experiments, we show
the effectiveness of our approach by reporting our competitive results on both
the AFEW-VA and SEWA datasets, suggesting that temporal modelling improves the
affect estimates both in qualitative and quantitative terms. Furthermore, we
find that the inclusion of attention mechanisms leads to the highest accuracy
improvements, as its weights seem to correlate well with the appearance of
facial movements, both in terms of temporal localisation and intensity.
Finally, we observe the sequence length of around 160\,ms to be the optimum one
for temporal modelling, which is consistent with other relevant findings
utilising similar lengths.
