Join our virtual meetup on September 24 to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.
Date, Time and Location
Sep 24, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!
How Do Mercedes-Benz AI Principles Drive our Innovation?
At Mercedes-Benz, our AI Principles guide every step of innovation, emphasizing responsible use, safety and reliability, explainability, and the protection of privacy. These principles go beyond statements and actively shape how we design, test, and deploy AI systems in real-world automotive and enterprise settings. In this talk, I will present how these principles inspired our recent research on when reusing LoRA (Low-Rank Adaptation) is effective. By combining theoretical analysis with synthetic data as a proxy for enterprise scenarios, we uncovered the strengths and limitations of modular AI components under constrained data access. Our findings provide practical guidance on when reused LoRAs could deliver high-quality results.
About the Speaker
Mei-Yen Chen is a Senior Data Scientist at Mercedes-Benz Tech Innovation GmbH in Germany with 10 years of industry experience in AI and data solutions. She leads early-stage AI projects across business functions and collaborates with research institutions on machine learning and responsible AI.
Region Tokens as the Visual Primitive: From Recognition to World Modeling
Patch-based tokenization has become the default interface between vision encoders and downstream models, yet patches carry no semantic structure and scale poorly with resolution and temporal extent. This talk presents a research program centered on replacing patch tokens with region-level representations — semantically dense tokens grounded in visual entities rather than arbitrary grid crops.
I will describe RELOCATE, REN, and T-REN, a progression of methods that produce region tokens via pooling, train them with region-level objectives, and extend them to video with temporal coher