Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model
This work addresses the problem of multi-view social behavior recognition in mice for neurodegenerative disease research, representing an incremental improvement over single-camera methods.
The paper tackles the challenge of identifying social behaviors in mice from multi-view video recordings by proposing a multiview latent-attention and dynamic discriminative model that learns view-specific and shared structures, achieving state-of-the-art performance on standard and Parkinson's disease datasets while handling imbalanced data.
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.