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.

本研究介绍了一种用于理解多个智能体之间互动的技术：多智能体行为建模，并提出了 CalMS21 数据集，可以用于行为研究中行为分类方法的性能评估，包括跟踪数据和带有注释的数据。