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2019


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EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association

Strecke, M., Stückler, J.

International Conference on Computer Vision, October 2019, arXiv:1904.11781 (conference) Accepted

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preprint [BibTex]

2019


preprint [BibTex]


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Variational Autoencoders Recover PCA Directions (by Accident)

Rolinek, M., Zietlow, D., Martius, G.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

Abstract
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way. We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder. The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen. Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.

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arXiv [BibTex]

arXiv [BibTex]


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Learning to Disentangle Latent Physical Factors for Video Prediction

Zhu, D., Munderloh, M., Rosenhahn, B., Stückler, J.

In German Conference on Pattern Recognition (GCPR), 2019, to appear (inproceedings)

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dataset & evaluation code video preprint [BibTex]

dataset & evaluation code video preprint [BibTex]


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3D Birds-Eye-View Instance Segmentation

Elich, C., Engelmann, F., Kontogianni, T., Leibe, B.

In German Conference on Pattern Recognition (GCPR), 2019, arXiv:1904.02199, to appear (inproceedings)

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[BibTex]

[BibTex]

2014


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Self-Exploration of the Stumpy Robot with Predictive Information Maximization

Martius, G., Jahn, L., Hauser, H., V. Hafner, V.

In Proc. From Animals to Animats, SAB 2014, 8575, pages: 32-42, LNCS, Springer, 2014 (inproceedings)

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[BibTex]

2014


[BibTex]