  Home-cage social behaviour analysis of mice is an invaluable tool to assess
therapeutic efficacy of neurodegenerative diseases. Despite tremendous efforts
made within the research community, single-camera video recordings are mainly
used for such analysis. Because of the potential to create rich descriptions of
mouse social behaviors, the use of multi-view video recordings for rodent
observations is increasingly receiving much attention. However, identifying
social behaviours from various views is still challenging due to the lack of
correspondence across data sources. To address this problem, we here propose a
novel multiview latent-attention and dynamic discriminative model that jointly
learns view-specific and view-shared sub-structures, where the former captures
unique dynamics of each view whilst the latter encodes the interaction between
the views. Furthermore, a novel multi-view latent-attention variational
autoencoder model is introduced in learning the acquired features, enabling us
to learn discriminative features in each view. Experimental results on the
standard CRMI13 and our multi-view Parkinson's Disease Mouse Behaviour (PDMB)
datasets demonstrate that our model outperforms the other state of the arts
technologies and effectively deals with the imbalanced data problem.
