MSGL-Transformer uses multi-scale attention mechanisms to capture rodent social behaviors across different temporal scales through short-range, medium-range, and global attention branches
The model introduces a Behavior-Aware Modulation (BAM) block inspired by SE-Networks to emphasize behavior-relevant features before attention processing
Tested on RatSI dataset (5 behavior classes, 12D pose inputs) and CalMS21 dataset (4 behavior classes, 28D pose inputs) for evaluation
Addresses limitations of traditional manual behavior scoring by reducing human error and significantly decreasing analysis time for neuroscience research
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