@article {6229,
	title = {Deep Unsupervised Learning of Visual Similarities},
	journal = {Pattern Recognition},
	volume = {78},
	year = {2018},
	chapter = {331},
	abstract = {Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.},
	doi = {https://doi.org/10.1016/j.patcog.2018.01.036},
	url = {https://authors.elsevier.com/a/1WXUt77nKSb25 },
	author = {Sanakoyeu, A. and Miguel Bautista and Bj{\"o}rn Ommer}
}
@conference {6250,
	title = {Learning to Forecast Pedestrian Intention from Pose Dynamics},
	booktitle = {Intelligent Vehicles, IEEE, 2018},
	year = {2018},
	author = {Omar Ghori and Radek Mackowiak and Miguel Bautista and Niklas Beuter and Lucas Drumond and Ferran Diego and Bj{\"o}rn Ommer}
}
@conference {6200,
	title = {Deep Unsupervised Similarity Learning using Partially Ordered Sets},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2017},
	author = {Miguel Bautista and Sanakoyeu, A. and Bj{\"o}rn Ommer}
}
@proceedings {6183,
	title = {Learning Where to Drive by Watching Others},
	volume = {1},
	year = {2017},
	publisher = {Springer-Verlag},
	address = {Basel},
	author = {Miguel Bautista and Fuchs, P. and Bj{\"o}rn Ommer}
}
@conference {6187,
	title = {Unsupervised Video Understanding by Reconciliation of Posture Similarities},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
	year = {2017},
	url = {https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17},
	author = {Timo Milbich and Miguel Bautista and Ekaterina Sutter and Bj{\"o}rn Ommer}
}
@conference {arXiv:1608.08792,
	title = {CliqueCNN: Deep Unsupervised Exemplar Learning},
	booktitle = {Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS)},
	year = {2016},
	publisher = {MIT Press},
	organization = {MIT Press},
	address = {Barcelona},
	abstract = {Exemplar learning is a powerful paradigm for discovering visual similarities in
an unsupervised manner.  In this context, however, the recent breakthrough in
deep learning could not yet unfold its full potential.  With only a single positive
sample, a great imbalance between one positive and many negatives, and unreliable
relationships between most samples, training of Convolutional Neural networks is
impaired. Given weak estimates of local distance we propose a single optimization
problem to extract batches of samples with mutually consistent relations. Conflict-
ing relations are distributed over different batches and similar samples are grouped
into compact cliques. Learning exemplar similarities is framed as a sequence of
clique categorization tasks. The CNN then consolidates transitivity relations within
and between cliques and learns a single representation for all samples without
the need for labels. The proposed unsupervised approach has shown competitive
performance on detailed posture analysis and object classification.},
	url = {https://arxiv.org/abs/1608.08792},
	author = {Miguel Bautista and Sanakoyeu, A. and Sutter, E. and Bj{\"o}rn Ommer}
}
