Thomas Hummel
PhD Researcher
Github Linkedin Website thomas.hummel at Google Scholar


Thomas Hummel is a PhD candidate in the Explainable Machine Learning group and the International Max-Planck Research School for Intelligent Systems (IMPRS-IS) under the supervision of Prof. Zeynep Akata. He received his master's degree in Intelligent Adaptive Systems from the University of Hamburg in 2019 and his bachelor's degree in Bioprocess Informatics from the Weihenstephan-Triesdorf University of Applied Sciences in 2015.

His primary research interests are in multi-modal learning and video understanding.


Video-adverb retrieval with compositional adverb-action embeddings
Thomas Hummel, Otniel-Bogdan Mercea, A. Sophia Koepke, Zeynep Akata
British Machine Vision Conference (BMVC), 2023
Paper / Project page / Code
Oral presentation.


Text-to-feature diffusion for audio-visual few-shot learning
Otniel-Bogdan Mercea, Thomas Hummel, A. Sophia Koepke, Zeynep Akata
DAGM German Conference on Pattern Recognition (GCPR), 2023
Paper / Code


Temporal and cross-modal attention for audio-visual zero-shot learning
Otniel-Bogdan Mercea*, Thomas Hummel*, A. Sophia Koepke, Zeynep Akata
European Conference on Computer Vision (ECCV), 2022
* equal contribution
Paper / Code


Semantic Image Synthesis with Semantically Coupled VQ-Model
Stephan Alaniz)* Thomas Hummel*, A. Sophia Koepke, Zeynep Akata
Workshop on Deep Generative Models for Highly Structured Data (DGM4HSD), ICLR 2022
* equal contribution

For a full and up-to-date list of publications, please also check Google Scholar.