uBAM: Unsupervised Behavior Analysis and Magnification using Deep Learning

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.

Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year. Although the field benefits from the rapid progress, the divergence in training protocols, architectures, and parameter choices make an unbiased comparison difficult. To provide a consistent reference point, we revisit the most widely used DML objective functions and conduct a study of the crucial parameter choices as well as the commonly neglected mini-batch sampling process. Based on our analysis, we uncover a correlation between the embedding space compression and the generalization performance of DML models. Exploiting these insights, we propose a simple, yet effective, training regularization to reliably boost the performance of ranking-based DML models on various standard benchmark datasets.