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Stuttgart AI, Machine Learning and Computer Vision Meetup
April 23, 2025 | 5:30 – 8:30 PM
Date and Time
April 23, 2025 from 5:30 PM to 8:30 PM
Location
Impact Hub Stuttgart, Quellenstraße 7a Stuttgart
Optimization and Individualization of Assisted Driving Functions based on Driver Feedback
Robin Schwager
Porsche AG
When using Advanced Driver Assistance Systems (ADAS), drivers often take over control of the active driving function to adjust its behavior to their own personal preferences. These takeovers can be used as feedback for the optimization and individualization of the driving function, if interpreted correctly.
In this presentation, a bottom-up data-driven approach is highlighted how ADAS optimization potential can be derived from real driving data. The underlying methodologies are developed by using the data of a test group study featuring naturalistic driving data. Then, a prototypical self-learning driving function is proposed, and its performance is evaluated in a second test group study.
Separation of Machine-Printed and Handwritten Text in Archival Documents
Mahsa Vafaie
FIZ Karlsruhe
One major challenge to date in the field of Document Processing is transforming analogue documents into computer-readable formats. Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) techniques are traditional methods for this transformation. Despite progress in text recognition through OCR and HTR, this issue remains largely unresolved, particularly regarding historical documents stored in archives, due to visual complexities such as overlapping areas, paper degradation, and ink fading. In the context of the project “Wiedergutmachung”, we propose a pipeline to address the issue of text type heterogeneity in single document images by decomposing the document into its constituent text types—handwritten and machine-printed text, to enhance text recognition accuracy by utilising appropriate models for each text layer, in order to improve the quality of final transcripts.
Using Knowledge Graphs to Harvest Datasets for Efficient CLIP Model Training
Simon Ging
University of Freiburg
Training high-quality CLIP models typically requires enormous datasets, which limits the development of domain-specific models and drives up training costs. In this work, we show that by employing image search based on knowledge graphs, a robust CLIP model can be trained from scratch with considerably less data. Specifically, we introduce the EntityNet dataset comprising 33M images paired with 46M text descriptions. We train an expert foundation model using a subset of 10M images of living organisms, and we train a generic CLIP model on the full dataset.
Practitioner’s Tricks for Image Labeling
Antonio Rueda-Toicen
Voxel51
This talk covers methods to label images in the main computer vision tasks:
- Classification (identifying objects)
- Segmentation (localizing at the pixel level)
- Detection (localizing with bounding boxes)
We look at combining zero-shot classifiers, like CLIP, with active learning. We will discuss key implementation details such as:
- Power and pitfalls of cross-entropy loss
- Model calibration methods
- Choices between single-class and multilabel classification methods
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The goal of the AI, Machine Learning, and Computer Vision Meetup network is to bring together a community of data scientists, machine learning engineers, and open source enthusiasts who want to share and expand their knowledge of AI and complementary technologies. If that’s you, we invite you to join the Meetup closest to your timezone.
Robin Schwager
Porsche AG
Robin Schwager is Research Engineer & PhD candidate at Porsche AG, working in the field of driver assistance systems and autonomous driving.
Mahsa Vafaie
FIZ Karlsruhe
Mahsa Vafaie is a PhD student and a researcher in the Information Service Engineering group at FIZ Karlsruhe– Leibniz Institute for Information Infrastructure. She is currently working on a digitalisation project that aims to create a knowledge graph for contextualisation of historical knowledge derived from collections of documents, records, and materials directly linked to the compensation process for the atrocities of the National Socialist regime in Germany (“Themenportal zur Wiedergutmachung nationalsozialistischen Unrechts”). She is also involved in scientific and teaching activities in the fields of Knowledge Graphs and Semantic Web, at Karlsruhe Institute of Technology (KIT) and in other conferences and venues relevant to these fields.
Simon Ging
University of Freiburg
Simon Ging is a PhD student at the Computer Vision Group, University of Freiburg. Interested in Vision-Language Models: Benchmarking, Contrastive Learning, Applications of VLMs in clinical radiology data.
Antonio Rueda-Toicen
Voxel51
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlin’s Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIA’s Deep Learning Institute.