Multi-Agent Social Behavior Understanding via Ego-GAT-SqueezeNet

Quantifying social behavior in laboratory animals is fundamental to neuroscience but remains hindered by manual annotation’s subjectivity. The Multi-Agent Behavior (MABe) challenge addresses this by benchmarking automated recognition from pose data, yet faces challenges like extreme class imbalance, complex topology, and cross-laboratory domain shifts.

In this work, we propose Ego-GAT-SqueezeNet, a unified framework for multi-agent behavior understanding. First, we introduce an egocentric alignment strategy to invariantize agent features against translation and rotation. Second, we employ a Graph Attention Network (GAT) to explicitly model the dynamic spatial topology. Crucially, we integrate a Squeezeformer backbone that leverages efficient downsampling to capture long-range dependencies in high-frequency sequences. For environmental heterogeneity, we utilize Feature-wise Linear Modulation (FiLM) to dynamically recalibrate features based on laboratory and subject identities. Our approach achieves an F1-score of 0.7702 on the validation set, outperforming baselines by identifying rare social actions across diverse experimental setups.