A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms.

ABSTRACT: Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms - which include tree-based models, deep neural networks, and graphical models - differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species - fly, mouse, and human - we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges

SUBMITTER: Blau A 

PROVIDER: S-EPMC12365852 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature