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2019
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.PDF icon Technical Report (137.6 KB)
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.PDF icon Technical Report (137.6 KB)
W. Li, Hosseini Jafari, O., and Rother, C., “Deep Object Co-segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 638–653.
L. Cerrone, Zeilmann, A., and Hamprecht, F. A., “End-to-End Learned Random Walker for Seeded Image Segmentation”, CVPR. Proceedings. pp. 12559-12568, 2019.
A. Imle, Kumberger, P., Schnellbächer, N. D., Fehr, J., Carillo-Bustamente, P., Ales, J., Schmidt, P., Ritter, C., Godinez, W. J., Müller, B., Rohr, K., Hamprecht, F. A., Schwarz, U. S., Graw, F., and Fackler, O. T., “Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures”, Nature Communications, vol. 13;10(1), 2019.
D. M. Kirchhöfer, Holst, G. A., Wouters, F. S., Hock, S., and Jähne, B., “Extended noise equalisation for image compression in microscopical applications”, tm - Technisches Messen, vol. 86, pp. 422–432, 2019.
D. M. Kirchhöfer, Holst, G. A., Wouters, F. S., Hock, S., and Jähne, B., “Extended noise equalisation for image compression in microscopical applications”, tm - Technisches Messen, vol. 86, pp. 422–432, 2019.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “ilastik: interactive machine learning for (bio)image analysis”, Nature Methods, vol. 16, pp. 1226-1232, 2019.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “ilastik: interactive machine learning for (bio)image analysis”, Nature Methods, vol. 16, pp. 1226-1232, 2019.
O. Hosseini Jafari, Mustikovela, S. Karthik, Pertsch, K., Brachmann, E., and Rother, C., “iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 477–492.
P. Hanslovsky, “Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron MIcroscopy”. Heidelberg University, 2019.
R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, in Proc. SSVM, 2019.
R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, preprint: arXiv, 2019.
E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., “LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos”, ICLR. Proceedings. 2019.
E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., “LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos”, ICLR. Proceedings. 2019.
W. Li, Hosseini Jafari, O., and Rother, C., “Localizing Common Objects Using Common Component Activation Map”, 2019.
F. E Sanmartin, Damrich, S., and Hamprecht, F. A., “Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning”, in Advances in Neural Information Processing Systems, 2019.
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation”, UAI. Proceedings. pp. 563-573, 2019.PDF icon Technical Report (1.04 MB)
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation”, UAI. Proceedings. pp. 563-573, 2019.PDF icon Technical Report (1.04 MB)
M. Esposito, Hennersperger, C., Göbl, R., Demaret, L., Storath, M., Navab, N., Baust, M., and Weinmann, A., “Total variation regularization of pose signals with an application to 3D freehand ultrasound”, IEEE Transactions on Medical Imaging, vol. 38(10), pp. 2245-2258, 2019.
P. Esser, Haux, J., and Ommer, B., “Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.
2018
T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., “BOP: Benchmark for 6D object pose estimation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.
T. Hehn and Hamprecht, F. A., “End-to-end Learning of Deterministic Decision Trees”, German Conference on Pattern Recognition. Proceedings, vol. LNCS 11269. Springer, pp. 612-627, 2018.PDF icon Technical Report (1.4 MB)
T. Hehn and Hamprecht, F. A., “End-to-end Learning of Deterministic Decision Trees”, German Conference on Pattern Recognition. Proceedings, vol. LNCS 11269. Springer, pp. 612-627, 2018.PDF icon Technical Report (1.4 MB)
F. Draxler, Veschgini, K., Salmhofer, M., and Hamprecht, F. A., “Essentially No Barriers in Neural Network Energy Landscape”, ICML. Proceedings, vol. 80. p. 1308--1317, 2018.PDF icon Technical Report (685.93 KB)
S. Haller, Swoboda, P., and Savchynskyy, B., “Exact MAP-Inference by Confining Combinatorial Search With LP Relaxation”, in Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, 2018.PDF icon 2018-02-02_aaai_dense_combilp.pdf (325.08 KB)
R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”, SIAM J. Imaging Science, vol. 11, pp. 1317–1362, 2018.
R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”, SIAM Journal on Imaging Sciences, vol. 11, no. 2, pp. 1317-1362, 2018.PDF icon Technical Report (2.62 MB)
O. Hosseini Jafari, Mustikovela, S. K., Pertsch, K., Brachmann, E., and Rother, C., “iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, ACCV. Proceedings, in press. 2018.PDF icon Technical Report (3.28 MB)
M. Weiler, Hamprecht, F. A., and Storath, M., “Learning Steerable Filters for Rotation Equivariant CNNs”, CVPR. Proceedings. pp. 849-858, 2018.PDF icon Technical Report (1.35 MB)
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, ECCV. Proceedings. Springer, pp. 571-587, 2018.
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 571–587.
N. Rahaman, Arpit, D., Baratin, A., Draxler, F., Lin, M., Hamprecht, F. A., Bengio, Y., and Courville, A., “On the spectral bias of deep neural networks”, arXiv preprint arXiv:1806.08734, 2018.
K. Bredies, Holler, M., Storath, M., and Weinmann, A., “Total Generalized Variation for Manifold-valued Data”, SIAM Journal on Imaging Sciences, vol. 11, no. 3, pp. 1785 - 1848, 2018.
P. Esser, Haux, J., Milbich, T., and Ommer, B., “Towards Learning a Realistic Rendering of Human Behavior”, in European Conference on Computer Vision (ECCV - HBUGEN), 2018.

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