@conference {buechler:CVPR:2017,
	title = {LSTM Self-Supervision for Detailed Behavior Analysis},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2017},
	publisher = {(BB and UB contributed equally)},
	organization = {(BB and UB contributed equally)},
	abstract = {Behavior analysis provides a crucial non-invasive and
easily accessible diagnostic tool for biomedical research.
A detailed analysis of posture changes during skilled mo-
tor tasks can reveal distinct functional deficits and their
restoration during recovery. Our specific scenario is based
on a neuroscientific study of rodents recovering from a large
sensorimotor cortex stroke and skilled forelimb grasping is
being recorded. Given large amounts of unlabeled videos
that are recorded during such long-term studies, we seek
an approach that captures fine-grained details of posture
and its change during rehabilitation without costly manual
supervision. Therefore, we utilize self-supervision to au-
tomatically learn accurate posture and behavior represen-
tations for analyzing motor function. Learning our model
depends on the following fundamental elements: (i) limb
detection based on a fully convolutional network is ini-
tialized solely using motion information, (ii) a novel self-
supervised training of LSTMs using only temporal permu-
tation yields a detailed representation of behavior, and (iii)
back-propagation of this sequence representation also im-
proves the description of individual postures. We establish a
novel test dataset with expert annotations for evaluation of
fine-grained behavior analysis. Moreover, we demonstrate
the generality of our approach by successfully applying it to
self-supervised learning of human posture on two standard
benchmark datasets.},
	author = {Biagio Brattoli and Uta B{\"u}chler and Anna-Sophia Wahl and M. E. Schwab and Bj{\"o}rn Ommer}
}
