no code implementations • 29 Jan 2024 • Richard Vogg, Timo Lüddecke, Jonathan Henrich, Sharmita Dey, Matthias Nuske, Valentin Hassler, Derek Murphy, Julia Fischer, Julia Ostner, Oliver Schülke, Peter M. Kappeler, Claudia Fichtel, Alexander Gail, Stefan Treue, Hansjörg Scherberger, Florentin Wörgötter, Alexander S. Ecker
With this perspective paper, we want to contribute towards closing this gap, by guiding behavioral scientists in what can be expected from current methods and steering computer vision researchers towards problems that are relevant to advance research in animal behavior.
1 code implementation • 24 Nov 2023 • Jonathan Roberts, Timo Lüddecke, Rehan Sheikh, Kai Han, Samuel Albanie
Multimodal large language models (MLLMs) have shown remarkable capabilities across a broad range of tasks but their knowledge and abilities in the geographic and geospatial domains are yet to be explored, despite potential wide-ranging benefits to navigation, environmental research, urban development, and disaster response.
1 code implementation • 9 Oct 2023 • Jan van Delden, Julius Schultz, Christopher Blech, Sabine C. Langer, Timo Lüddecke
To quantify such trade-offs systematically and foster the development of methods, we present a benchmark on the task of predicting the vibration of harmonically excited plates.
no code implementations • 30 May 2023 • Jonathan Roberts, Timo Lüddecke, Sowmen Das, Kai Han, Samuel Albanie
Large language models (LLMs) have shown remarkable capabilities across a broad range of tasks involving question answering and the generation of coherent text and code.
no code implementations • 23 Dec 2021 • Marissa A. Weis, Laura Hansel, Timo Lüddecke, Alexander S. Ecker
GraphDINO is a novel transformer-based representation learning method for spatially-embedded graphs.
4 code implementations • CVPR 2022 • Timo Lüddecke, Alexander S. Ecker
After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query.
Ranked #3 on Referring Image Matting (Keyword-based) on RefMatte
no code implementations • NeurIPS Workshop SVRHM 2020 • Timo Lüddecke, Alexander S Ecker
The role of feedback (or recurrent) connections is a fundamental question in neuroscience and machine learning.
no code implementations • 1 Apr 2020 • Tomas Kulvicius, Sebastian Herzog, Timo Lüddecke, Minija Tamosiunaite, Florentin Wörgötter
In contrast to that, we propose a novel method by utilising fully convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i. e., with a single prediction step.
no code implementations • 14 Nov 2019 • Lukas Hahne, Timo Lüddecke, Florentin Wörgötter, David Kappel
Our proposed hybrid model, represents an alternative on learning abstract relations using self-attention and demonstrates that the Transformer network is also well suited for abstract visual reasoning.
no code implementations • 26 Sep 2017 • Timo Lüddecke, Florentin Wörgötter
An autonomous robot should be able to evaluate the affordances that are offered by a given situation.