75 citations · 78 across the 6 of their papers we have counts for
6 papers
Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology
Yusuf Salcan, Simon Ging, Robin Tibor Schirrmeister +4
We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations. We introduce RefRad2D, a large-scale bilingual (German/Engli…
Learning to Read Where to Look: Disease-Aware Vision-Language Pretraining for 3D CT
Simon Ging, Philipp Arnold, Sebastian Walter +6
Recent 3D CT vision-language models align volumes with reports via contrastive pretraining, but typically rely on limited public data and provide only coarse global supervision. We…
Using Knowledge Graphs to harvest datasets for efficient CLIP model training
Simon Ging, Sebastian Walter, Jelena Bratulić +3
Training high-quality CLIP models typically requires enormous datasets, which limits the development of domain-specific models -- especially in areas that even the largest CLIP mod…
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
Simon Ging, María A. Bravo, Thomas Brox
The evaluation of text-generative vision-language models is a challenging yet crucial endeavor. By addressing the limitations of existing Visual Question Answering (VQA) benchmarks…
Open-vocabulary Attribute Detection
María A. Bravo, Sudhanshu Mittal, Simon Ging +1
Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on obj…
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash +1
Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In th…