Cross-Domain and Cross-Dimension Learning for Image-to-Graph Transformers
MCML Authors
Georgios Kaissis
Dr.
Associate
* Former Associate
Martin Menten
Dr.
JRG Leader AI for Vision
Daniel Rückert
Prof. Dr.
Director
Abstract
Georgios Kaissis
Dr.
Associate
* Former Associate
Martin Menten
Dr.
JRG Leader AI for Vision
Daniel Rückert
Prof. Dr.
Director
Abstract
Direct image-to-graph transformation is a challenging task that involves solving object detection and relationship prediction in a single model. Due to this task's complexity, large training datasets are rare in many domains, making the training of deep-learning methods challenging. This data sparsity necessitates transfer learning strategies akin to the state-of-the-art in general computer vision. In this work, we introduce a set of methods enabling cross-domain and cross-dimension learning for image-to-graph transformers. We propose (1) a regularized edge sampling loss to effectively learn object relations in multiple domains with different numbers of edges, (2) a domain adaptation framework for image-to-graph transformers aligning image- and graph-level features from different domains, and (3) a projection function that allows using 2D data for training 3D transformers. We demonstrate our method's utility in cross-domain and cross-dimension experiments, where we utilize labeled data from 2D road networks for simultaneous learning in vastly different target domains. Our method consistently outperforms standard transfer learning and self-supervised pretraining on challenging benchmarks, such as retinal or whole-brain vessel graph extraction.
inproceedings
WACV 2025
IEEE/CVF Winter Conference on Applications of Computer Vision. Tucson, AZ, USA, Feb 28-Mar 04, 2025.
Authors
A. H. Berger • L. Lux • S. Shit • I. Ezhov • G. Kaissis • M. J. Menten • D. Rückert • J. C. PaetzoldLinks
DOIResearch Area
BibTeXKey: BLS+25