Publications
2019
Berg, S, 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 (2019).
ilastik: interactive machine learning for (bio)image analysis.
Nature Methods.
16 1226-1232
Berg, S, 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 (2019).
ilastik: interactive machine learning for (bio)image analysis.
Nature Methods.
16 1226-1232
Berg, S, 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 (2019).
ilastik: interactive machine learning for (bio)image analysis.
Nature Methods.
16 1226-1232
Hosseini Jafari, O, Mustikovela, S Karthik, Pertsch, K, Brachmann, E and Rother, C (2019).
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
11363 LNCS 477–492
Bengio, Y, Deleu, T, Rahaman, N, Ke, R, Lachapelle, S, Bilaniuk, O, Goyal, A and Pal, C (2019).
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.
arXiv preprint arXiv:1901.10912
Technical Report (871.59 KB) Bengio, Y, Deleu, T, Rahaman, N, Ke, R, Lachapelle, S, Bilaniuk, O, Goyal, A and Pal, C (2019).
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.
arXiv preprint arXiv:1901.10912
Technical Report (871.59 KB) Brachmann, E and Rother, C (2019).
Neural-guided RANSAC: Learning where to sample model hypotheses.
Proceedings of the IEEE International Conference on Computer Vision.
2019-Octob 4321–4330.
http://arxiv.org/abs/1905.04132
PDF (8.02 MB) Esposito, M, Hennersperger, C, Göbl, R, Demaret, L, Storath, M, Navab, N, Baust, M and Weinmann, A (2019).
Total variation regularization of pose signals with an application to 3D freehand ultrasound.
IEEE Transactions on Medical Imaging.
38(10) 2245-2258
Lorenz, D, Bereska, L, Milbich, T and Ommer, B (2019).
Unsupervised Part-Based Disentangling of Object Shape and Appearance.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions) 2020
Wolny, A, Cerrone, L, Vijayan, A, Tofanelli, R, Vilches-Barro, A, Louveaux, M, Wenzel, C, Strauss, S, Wilson-Sanchez, D, Lymbouridou, R, Steigleder, S S, Pape, C, Bailoni, A, Duran-Nebreda, S, Bassel, G W, Lohmann, J U, Tsiantis, M, Hamprecht, F A, Schneitz, K, Maizel, A and Kreshuk, A (2020).
Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution.
eLife.
9 Wolny, A, Cerrone, L, Vijayan, A, Tofanelli, R, Vilches-Barro, A, Louveaux, M, Wenzel, C, Strauss, S, Wilson-Sanchez, D, Lymbouridou, R, Steigleder, S S, Pape, C, Bailoni, A, Duran-Nebreda, S, Bassel, G W, Lohmann, J U, Tsiantis, M, Hamprecht, F A, Schneitz, K, Maizel, A and Kreshuk, A (2020).
Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution.
eLife.
9 Kluger, F, Brachmann, E, Ackermann, H, Rother, C, Yang, M Ying and Rosenhahn, B (2020).
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus.
CVPR 2020.
http://arxiv.org/abs/2001.02643
PDF (9.95 MB) Bollweg, S, Haußmann, M, Kasieczka, G, Luchmann, M, Plehn, T and Thompson, J (2020).
Deep-Learning Jets with Uncertainties and More.
SciPost Phys.
8.
https://scipost.org/10.21468/SciPostPhys.8.1.006
Technical Report (1.65 MB) Milbich, T, Roth, K, Bharadhwaj, H, Sinha, S, Bengio, Y, Ommer, B and Cohen, J Paul (2020).
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning.
IEEE European Conference on Computer Vision (ECCV).
https://arxiv.org/abs/2004.13458 Milbich, T, Roth, K, Bharadhwaj, H, Sinha, S, Bengio, Y, Ommer, B and Cohen, J Paul (2020).
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning.
IEEE European Conference on Computer Vision (ECCV).
https://arxiv.org/abs/2004.13458 Schilling, H, Gutsche, M, Brock, A, Späth, D, Rother, C and Krispin, K (2020).
Mind the Gap – A Benchmark for Dense Depth Prediction beyond Lidar.
2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020 Wolf, S, Bailoni, A, Pape, C, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2020).
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning.
IEEE Transactions on Pattern Analysis and Machine Intelligence.
43 3724-3738
Technical Report (2.58 MB) Wolf, S, Li, Y, Pape, C, Bailoni, A, Kreshuk, A and Hamprecht, F A (2020).
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation.
ECCV. Proceedings. 208-224
Dorkenwald, M, Büchler, U and Ommer, B (2020).
Unsupervised Magnification of Posture Deviations Across Subjects.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
article.pdf (1.15 MB) Pages