# Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model

arXiv: https://arxiv.org/abs/2011.02451 · HTML: https://mlsanity.com/paper/2011.02451/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour](https://mlsanity.com/paper/2208.03819/) (2022)
- [Animal Behavior Analysis Methods Using Deep Learning: A Survey](https://mlsanity.com/paper/2405.14002/) (2024)
- [Multi-view knowledge distillation transformer for human action recognition](https://mlsanity.com/paper/2303.14358/) (2023)
- [SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action Segmentation](https://mlsanity.com/paper/2312.12347/) (2023)
- [Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups using a Single Model across Cages](https://mlsanity.com/paper/2306.03066/) (2023)

## Abstract (arXiv)

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.