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- W3111155973 abstract "Motor behavior analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. State-of-the-art instrumented movement analysis is time- and cost-intensive, since it requires placing physical or virtual markers. Besides the effort required for marking keypoints or annotations necessary for training or finetuning a detector, users need to know the interesting behavior beforehand to provide meaningful keypoints. We introduce uBAM, a novel, automatic deep learning algorithm for behavior analysis by discovering and magnifying deviations. We propose an unsupervised learning of posture and behavior representations that enable an objective behavior comparison across subjects. A generative model with novel disentanglement of appearance and behavior magnifies subtle behavior differences across subjects directly in a video without requiring a detour via keypoints or annotations. Evaluations on rodents and human patients with neurological diseases demonstrate the wide applicability of our approach." @default.
- W3111155973 created "2020-12-21" @default.
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- W3111155973 date "2020-12-16" @default.
- W3111155973 modified "2023-09-27" @default.
- W3111155973 title "uBAM: Unsupervised Behavior Analysis and Magnification using Deep Learning." @default.
- W3111155973 hasPublicationYear "2020" @default.
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