Associated

2019

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
Hanslovsky, P (2019). Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron MIcroscopy. Heidelberg University
Hühnerbein, R, Savarino, F, Petra, S and Schnörr, C (2019). Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. Proc. SSVM. Springer
Hühnerbein, R, Savarino, F, Petra, S and Schnörr, C (2019). Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. preprint: arXiv. https://arxiv.org/abs/1910.09976
Leistner, T, Schilling, H, Mackowiak, R, Gumhold, S and Rother, C (2019). Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift. Proceedings - 2019 International Conference on 3D Vision, 3DV 2019. 249–257. http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036PDF icon PDF (8.94 MB)
Kirschbaum, E, Haußmann, M, Wolf, S, Sonntag, H, Schneider, J, Elzoheiry, S, Kann, O, Durstewitz, D and Hamprecht, F A (2019). LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR. Proceedings
Li, W, Hosseini Jafari, O and Rother, C (2019). Localizing Common Objects Using Common Component Activation Map
Peter, S (2019). Machine learning under test-time budget constraints. Heidelberg University
Nagel, L, Krall, K E and Jähne, B (2019). Measurement of air-sea gas transfer velocities in the Baltic Sea. Ocean Science. 15 235–247
Brattoli, B, Roth, K and Ommer, B (2019). MIC: Mining Interclass Characteristics for Improved Metric Learning. Proceedings of the Intl. Conf. on Computer Vision (ICCV)
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/1905.04132PDF icon PDF (8.02 MB)
Ravindran, A (2019). Novel Deep Learning-Based Instance Segmentation Using Mutex Watershed For Microscopy Cell Images. Heidelberg University
Kirschbaum, E (2019). Novel Machine Learning Approaches for Neurophysiological Data Analysis. Heidelberg University
Adler, T J, Ayala, L, Ardizzone, L, Kenngott, H G, Vemuri, A, Müller-Stich, B P, Rother, C, Köthe, U and Maier-Hein, L (2019). Out of Distribution Detection for Intra-operative Functional Imaging. MICCAI UNSURE Workshop 2019. 11840 LNCS 75–82PDF icon PDF (3.1 MB)
Snajder, R (2019). Pipeline Für Die Automatisierte Objektsegmentierung Von 3D Lightshet Mikroskopiebildern. Heidelberg University
E Sanmartin, F, Damrich, S and Hamprecht, F A (2019). Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. Advances in Neural Information Processing Systems
Bhowmik, A, Gumhold, S, Rother, C and Brachmann, E (2019). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. http://arxiv.org/abs/1912.00623
Li, J (2019). Robust Single Object Tracking Via Fully Convolutional Siamese Networks. Heidelberg University
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. Proceedings. 563-573PDF icon Technical Report (1.04 MB)
Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Self-Assignment Flows for Unsupervised Data Labeling on Graphs. preprint: arXiv. https://arxiv.org/abs/1911.03472
Li, Y (2019). Semantic Instance Segmentation With The Multiway Mutex Watershed. Heidelberg University
Fita, E (2019). Semi-Supervised Distance-Based Segmentation. Heidelberg University
Voigt, P (2019). Simulation And Measurement Of The Water-Sided Viscous Shear Stress Without Waves. Institut für Umweltphysik, Universität Heidelberg, Germany
Storath, M, Kiefer, L and Weinmann, A (2019). Smoothing for signals with discontinuities using higher order Mumford-Shah models. Numerische Mathematik. 143(2) 423-460PDF icon Technical Report (1.09 MB)
Desana, M and Schnörr, C (2019). Sum-Product Graphical Models. Machine Learning. https://doi.org/10.1007/s10994-019-05813-2
Censor, Y, Petra, S and Schnörr, C (2019). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. preprint: arXiv. https://arxiv.org/abs/1911.05498
Großkinsky, (2019). Synaptic Cleft Prediction On Electron Microsope Images. Heidelberg University
Klein, A (2019). The Fetch Dependency of Small-Scale Air-Sea Interaction Processes at Low to Moderate Wind Speeds. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, Heidelberg. Dissertation
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
Xiao, S (2019). Tracking Dividing Cells Using Spatio-Temporal Embeddings. Heidelberg University
Zern, A, Zisler, M, Petra, S and Schnörr, C (2019). Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. preprint: arXiv. https://arxiv.org/abs/1904.10863
Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment. Proc. SSVM. Springer
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)
Esser, P, Haux, J and Ommer, B (2019). Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis. Proceedings of the Intl. Conf. on Computer Vision (ICCV). https://compvis.github.io/robust-disentangling/
Kotovenko, D, Sanakoyeu, A, Lang, S, Ma, P and Ommer, B (2019). Using a Transformation Content Block For Image Style Transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ufer, N, Lui, K To, Schwarz, K, Warkentin, P and Ommer, B (2019). Weakly Supervised Learning of Dense SemanticCorrespondences and Segmentation. German Conference on Pattern Recognition (GCPR)PDF icon article (6.1 MB)
Pandey, N (2019). Weakly Supervised Semantic Segmentation. Heidelberg University

2018

Sanakoyeu, A, Kotovenko, D, Lang, S and Ommer, B (2018). A Style-Aware Content Loss for Real-time HD Style Transfer. Proceedings of the European Conference on Computer Vision (ECCV) (Oral)
(2018). A Supplementary Material Cereals-Cost-Effective Region-Based Active Learning For Semantic Segmentation
Esser, P, Sutter, E and Ommer, B (2018). A Variational U-Net for Conditional Appearance and Shape Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (short Oral). https://compvis.github.io/vunet/

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