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AI, ML and Computer Vision Meetup — September 24

Event artwork for AI, ML and Computer Vision Meetup — September 24

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About this event

What to expect

A two-hour virtual meetup on 24 September 2026 brings together four expert talks spanning responsible AI, visual modelling, generative imaging and agricultural computer vision.

Hosted by the San Francisco AI Meetup Group, this online event runs from 9:00 AM to 11:00 AM PST. It offers a research-led look at how machine learning and computer vision techniques move from theory into automotive, creative and agricultural applications.

Talks and speakers

  • How Do Mercedes-Benz AI Principles Drive Our Innovation? , Mei-Yen Chen, Senior Data Scientist at Mercedes-Benz Tech Innovation GmbH, will explain how responsible use, safety, reliability, explainability and privacy influence the design and deployment of AI. She will connect these principles to research into when reusing LoRA components is effective, including their strengths and limitations when access to data is constrained.

  • Region Tokens as the Visual Primitive: From Recognition to World Modeling , Savya Khosla, a PhD student at the University of Illinois Urbana-Champaign, will challenge patch-based visual tokenisation. The talk covers RELOCATE, REN and T-REN, the progression from region-level pooling and objectives to temporally coherent video representations, as well as work on VLM context capacity and future region trajectory prediction.

  • Leveraging Text-to-Image Diffusion Models for Consistent Set-to-Set Generation , Kate Feingold, a Computer Vision PhD student at the Weizmann Institute of Science, will present Match-and-Fuse. This zero-shot, training-free approach uses dense correspondences and priors from text-to-image diffusion models to generate coherent outputs from image collections captured across different viewpoints, times and surroundings.

  • Yield Estimation of Coffee in a Dense Environment , Raghu M. Rao, a computer vision AI consultant, will walk through a real-world coffee yield estimation workflow. Using smartphone images from coffee estates, he will cover preprocessing, ROI annotation, object detection with YOLO models, and segmentation with SAM2 and YOLO-seg to count berries by maturity and estimate estate-wide yield.…

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