Timo Milbich Ph.D.

Postdoctoral Researcher at LMU Munich & Heidelberg University

I actively contribute to research on Computer Vision and (Deep) Machine Learning as a Postdoctoral Researcher in the Machine Vision and Learning Group at LMU Munich. My interest in and passion for processing and analyzing visual data sparked during my studies in Mathematics and Scientifc Computing, which I fully dedicated to the areas of Computer Vision, Machine Learning and Optimization. Since then I have been able to explore these areas further while pursuing a Ph.D. in Computer Vision at the Heidelberg Collaboratory for Image Processing (HCI) - one of Germany’s biggest and prestigious institutes in this area. My work focuses on learning representations for and similarities between images, as well as analyzing human poses and their dynamics.

Aside from my research and teaching duties, I love to play Volleyball with my team and friends, hike and get lost in nature around the globe or just spend my time in cafes reading books and learning new things.


Mar 10, 2022 ❗ We will give a tutorial on Deep Visual Similarity and Metric Learning at CVPR’22. ❗
Sep 28, 2021 Paper accepted at NeuRIPS 2021.
Jul 22, 2021 Paper accepted at ICCV 2021.
Feb 28, 2021 Three papers accepted at CVPR 2021.

selected publications

(For full list, please see 'publications' or Google Scholar profile.)

  1. DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
    Milbich*, T., Roth*, K., Bharadhwaj, H., Sinha, S., Bengio, Y., Ommer, B., and Cohen, J. P.
    In European Conference on Computer Vision (ECCV) 2020
  2. Sharing Matters for Generalization in Deep Metric Learning
    Milbich*, T., Roth*, K., Brattoli, B., and Ommer, B.
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2020
  3. Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
    Milbich*, T., Roth*, K., Sinha, S., Schmidt, L., Ghassemi, M., and Ommer, B.
    In Advances in Neural Information Processing Systems (NeurIPS) 2021
  4. Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
    Roth*, K.,  Milbich*, T., Sinha, S., Gupta, P., Ommer, B., and Cohen, J. P.
    In International Conference on Machine Learning (ICML) 2020
  5. Unsupervised Part-based Disentangling of Object Shape and Appearance
    Lorenz, D., Bereska, L.,  Milbich, T., and Ommer, B.
    In Conference on Computer Vision and Pattern Recognition (CVPR) 2019
  6. Unsupervised Video Understanding by Reconciliation of Posture Similarities
    Milbich, T., Bautista, M., Sutter, E., and Ommer, B.
    In International Conference on Computer Vision 2017
  7. Behavior-Driven Synthesis of Human Dynamics
    Milbich*, T., Blattmann*, A., Dorkenwald*, M., and Ommer, B.
    In Conference on Computer Vision and Pattern Recognition (CVPR) 2021