The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
This dataset addresses the need for standardized benchmarks in multi-agent behavior modeling for behavioral neuroscience, particularly for studying mouse social interactions, but it is incremental as it builds on existing data collection methods.
The authors introduced the CalMS21 dataset, a multi-agent dataset from behavioral neuroscience containing 6 million frames of unlabeled tracked poses and over 1 million frames with annotations, to benchmark automated behavior classification methods in three settings, including training on large datasets and learning new behaviors with limited data.
Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay. To help accelerate behavioral studies, the CalMS21 dataset provides benchmarks to evaluate the performance of automated behavior classification methods in three settings: (1) for training on large behavioral datasets all annotated by a single annotator, (2) for style transfer to learn inter-annotator differences in behavior definitions, and (3) for learning of new behaviors of interest given limited training data. The dataset consists of 6 million frames of unlabeled tracked poses of interacting mice, as well as over 1 million frames with tracked poses and corresponding frame-level behavior annotations. The challenge of our dataset is to be able to classify behaviors accurately using both labeled and unlabeled tracking data, as well as being able to generalize to new settings.