Computer Vision and Machine Learning

In the Focus Group Computer Vision & Machine Learning, Carl von Linde Senior Fellow Prof. Daniel Cremers (TUM) works together with Rudolf Diesel Industry Fellow Prof. Michael Bronstein (Intel / Imperial College / University of Lugano).

The activities of the Computer Vision and Machine Learning focus group is primarily centered around the interplay between geometry, machine learning and computer vision. Analysis of geometric objects has been a topic of computer vision and pattern recognition since the inception of the field. Classical computer vision problems of “Shape-from-X” aim at recovering the geometric structure of a 3D object from multiple images (Shape from stereo) or different illumination conditions (photometric stereo). In the recent years, the interest in 3D data has increased dramatically, fuelled in part by the commercial availability of affordable and compact 3D sensors. Such sensors are nowadays found in a broad range of applications from drones and augmented reality to self-driving cars.

In July 2018, the Focus Group Computer Vision & Machine Learning organized the workshop Machine Learning for 3D Understanding.

Publications by the Focus Group

2020

  • Anees Kazi, Luca Cosmo, Nassir Navab, Michael Bronstein: Differentiable Graph Module (DGM) Graph Convolutional Networks. 2020 mehr…

2019

  • Bouritsas, Giorgos; Bokhnyak, Sergiy; Ploumpis, Stylianos; Zafeiriou, Stefanos; Bronstein, Michael: Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, 2019 mehr…
  • Dyke, R. M.; Stride, C.; Lai, Y.-K.; Rosin, P. L.; Aubry, M.; Boyarski, A.; Bronstein, A. M.; Bronstein, M. M.; Cremers, D.; Fisher, M.; Groueix, T.; Guo, D.; Kim, V. G.; Kimmel, R.; Lähner, Z.; Li, K.; Litany, O.; Remez, T.; Rodolà, E.; Russell, B. C.; Sahillioglu, Y.; Slossberg, R.; Tam, G. K. L.; Vestner, M.; Wu, Z.; Yang, J.: Shape Correspondence with Isometric and Non-Isometric Deformations. Eurographics Workshop on 3D Object Retrieval, 2019 mehr…
  • Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein: Fake News Detection on Social Media using Geometric Deep Learning. 2019 mehr…
  • Gainza, P.; Sverrisson, F.; Monti, F.; Rodolà, E.; Boscaini, D.; Bronstein, M. M.; Correia, B. E.: Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods, 2019 mehr…
  • Levie, Ron; Monti, Federico; Bresson, Xavier; Bronstein, Michael M.: CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters. IEEE Transactions on Signal Processing 67 (1), 2019, 97-109 mehr…
  • Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixe: CVPR19 Tracking and Detection Challenge: How crowded can it get? 2019 mehr…
  • Qadeer Khan, Patrick Wenzel, Daniel Cremers, Laura Leal-Taixé: Towards Generalizing Sensorimotor Control Across Weather Conditions. 2019 mehr…
  • Rodolà, E.; Lähner, Z.; Bronstein, A. M.; Bronstein, M. M.; Solomon, J.: Functional Maps Representation On Product Manifolds. Computer Graphics Forum 38 (1), 2019, 678-689 mehr…
  • Veselkov, Kirill; Gonzalez, Guadalupe; Aljifri, Shahad; Galea, Dieter; Mirnezami, Reza; Youssef, Jozef; Bronstein, Michael; Laponogov, Ivan: HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods. Scientific Reports 9 (1), 2019 mehr…
  • Wang, Yue; Sun, Yongbin; Liu, Ziwei; Sarma, Sanjay E.; Bronstein, Michael M.; Solomon, Justin M.: Dynamic Graph CNN for Learning on Point Clouds. ACM Transactions on Graphics 38 (5), 2019, 1-12 mehr…

2018

  • Choma, Nicholas; Monti, Federico; Gerhardt, Lisa; Palczewski, Tomasz; Ronaghi, Zahra; Prabhat, Prabhat; Bhimji, Wahid; Bronstein, Michael; Klein, Spencer; Bruna, Joan: Graph Neural Networks for IceCube Signal Classification. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2018 mehr…
  • Gehre, Anne; Lim, Isaak; Kobbelt, Leif: Feature Curve Co-Completion in Noisy Data. Computer Graphics Forum 37 (2), 2018, 1-12 mehr…
  • Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas: PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. 2018 mehr…
  • Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas: PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. Proceedings of the International Conference on Learning Representations, 2018 mehr…
  • Litany, Or; Bronstein, Alex; Bronstein, Michael; Makadia, Ameesh: Deformable Shape Completion with Graph Convolutional Autoencoders. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018 mehr…
  • Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixé, Nils Thuerey: Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation. 2018 mehr…
  • Monti, Federico; Otness, Karl; Bronstein, Michael M.: MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS. 2018 IEEE Data Science Workshop (DSW), IEEE, 2018 mehr…
  • Nogneng, D.; Melzi, S.; Rodolà, E.; Castellani, U.; Bronstein, M.; Ovsjanikov, M.: Improved Functional Mappings via Product Preservation. Computer Graphics Forum 37 (2), 2018, 179-190 mehr…

2017

  • Melzi, S.; Rodolà, E.; Castellani, U.; Bronstein, M. M.: Localized Manifold Harmonics for Spectral Shape Analysis. Computer Graphics Forum 37 (6), 2017, 20-34 mehr…