Austin AI, Machine Learning and Computer Vision Meetup

Register for the Zoom

When and Where

• May 20, 2025
• 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
• Workshops are delivered over Zoom

About the Workshop

Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.

In this session, we’ll explore image generation techniques using diffusion models. Participants will build a U-Net-based model to generate MNIST-like images and then inspect the generated outputs with FiftyOne.

These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)

Workshop Resources

You can find the workshop materials in this GitHub repository.

About the Instructor

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.

Virtually over Zoom. Sign up!

About the Workshop
Are you looking for simpler and more accurate ways to perform common data curation and model evaluation tasks for your computer vision workflows?

Then this workshop with Harpreet Sahota is for you! In this 90 min hands-on workshop, we’ll show you how to make use of FiftyOne’s panel and plugin framework to learn how to:

• Customize the FiftyOne App to work the way you want to work
• Quickly integrate FiftyOne with new models, datasets, and MLOps tools
• Automate common data curation and model evaluation tasks
• Streamline your computer vision workflows with less code and more clicks

Whether you are a beginner or advanced user of FiftyOne, looking for how to get started with customizing the dozens of existing plugins or interested in creating your own, there will be something for you in this workshop!

Prerequisites

A working knowledge of Python and basic familiarity with FiftyOne. All attendees will get access to the tutorials, videos, and code examples used in the workshop.

• May 22, 2025 | 10:00 AM Pacific
• Virtual - Register for the Zoom

CountGD: Multi-Modal Open-World Counting

We propose CountGD, the first open-world counting model that can count any object specified by text only, visual examples only, or both together. CountGD extends the Grounding DINO architecture and adds components to enable specifying the object with visual examples. This new capability – being able to specify the target object by multi-modalites (text and exemplars) – lead to an improvement in counting accuracy. CountGD is powering multiple products and has been applied to problems across different domains including counting large populations of penguins to monitor the influence of climate change, counting buildings from satellite images, and counting seals for conservation.

About the Speaker

Niki Amini-Naieni is a DPhil student focusing on developing foundation model capabilities for visual understanding of the open world at the Visual Geometry Group (VGG), Oxford supervised by Andrew Zisserman. In the past, Niki has consulted with Amazon and other companies in robotics and computer vision, interned at SpaceX, and studied computer science and engineering at Cornell.

GorillaWatch: Advancing Gorilla Re-Identification and Population Monitoring with AI

Accurate monitoring of endangered gorilla populations is critical for conservation efforts in the field, where scientists currently rely on labor-intensive manual video labeling methods. The GorillaWatch project applies visual AI to provide robust re-identification of individual gorillas and generate local population estimates in wildlife encounters.

About the Speaker

Maximilian von Klinski is a Computer Science student at the Hasso-Plattner-Institut and is currently working on the GorillaWatch project alongside seven fellow students.

This Gets Under Your Skin – The Art of Skin Type Classification

Skin analysis is deceptively hard: inconsistent portrait quality, lighting variations, and the presence of sunscreen or makeup often obscure what’s truly “under the skin.” In this talk, I’ll share how we built an AI pipeline for skin type classification that tackles these real-world challenges with a combination of vision models. The architecture includes image quality control, facial segmentation, and a final classifier trained on curated dermatological features.

About the Speaker

Markus Hinsche is the co-founder and CTO of Thea Care, where he builds AI-powered skincare solutions at the intersection of health, beauty, and longevity. He holds a Master’s in Software Engineering from the Hasso Plattner Institute and brings a deep background in AI and product development.

A Spot Pattern Is like a Fingerprint: Jaguar Identification Project

The Jaguar Identification Project is a citizen science initiative actively engaging the public in conservation efforts in Porto Jofre, Brazil. This project increases awareness and provides an interesting and challenging dataset that requires the use of fine-grained visual classification algorithms. We use this rich dataset for dual purposes: teaching data-centric visual AI and directly contributing to conservation efforts for this vulnerable species.

Learn more: Jaguar Identification Project | Jaguar Conservation NGO in Brazil | Porto Jofre – Poconé, State of Mato Grosso, Brazil

About the Speaker

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.

Register for the Zoom

Welcome to the Best of WACV 2025 virtual series that highlights some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials.

DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models

Given a small number of images of a subject, personalized image generation techniques can fine-tune large pre-trained text-to-image diffusion models to generate images of the subject in novel contexts, conditioned on text prompts. In doing so, a trade-off is made between prompt fidelity, subject fidelity and diversity. As the pre-trained model is fine-tuned, earlier checkpoints synthesize images with low subject fidelity but high prompt fidelity and diversity. In contrast, later checkpoints generate images with low prompt fidelity and diversity but high subject fidelity. This inherent trade-off limits the prompt fidelity, subject fidelity and diversity of generated images. In this work, we propose DreamBlend to combine the prompt fidelity from earlier checkpoints and the subject fidelity from later checkpoints during inference. We perform a cross attention guided image synthesis from a later checkpoint, guided by an image generated by an earlier checkpoint, for the same prompt. This enables generation of images with better subject fidelity, prompt fidelity and diversity on challenging prompts, outperforming state-of-the-art fine-tuning methods.

Paper: DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models

About the Speaker

Shwetha Ram is an Applied Scientist at Amazon, where she focuses on advancing multimodal capabilities for Rufus, Amazon’s generative AI-powered conversational shopping assistant. Her work has contributed to a range of innovative initiatives across Amazon, including Lab126, Scout (the autonomous sidewalk delivery robot), and M5 (Amazon’s foundation models). Prior to joining Amazon, Shwetha was part of the Image Technology Incubation team at Dolby Laboratories, where she explored emerging opportunities for Dolby in AR/ VR and immersive media technologies.

Robust Multi-Class Anomaly Detection under Domain Shift

Robust multi-class anomaly detection under domain shift is a fundamental challenge in real-world scenarios, where detectors should distinguish different types of anomalies despite significant distribution shifts. Traditional approaches often struggle to generalize across domains and handle inter-class interference. ROADS addresses these limitations through a prompt-driven framework that combines a hierarchical class-aware prompt mechanism with a domain adapter to jointly encode discriminative, class-specific prompts and learn domain-invariant representations. Extensive evaluations on the MVTec-AD and VISA datasets show that ROADS achieves superior performance in both anomaly detection and localization, particularly in out-of-distribution settings.

Paper: ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift

About the Speaker

Hossein Kashiani is a fourth-year Ph.D. student at Clemson University. His research focuses on developing generalizable and trustworthy AI systems, with publications in top venues such as CVPR (2025), WACV (2025), ICIP, and TBIOM. His work spans diverse applications, including anomaly detection, media forensics, biometrics, healthcare, and visual perception.

What Remains Unsolved in Computer Vision? Rethinking the Boundaries of State-of-the-Art

Despite rapid progress and increasingly powerful models, computer vision still struggles with a range of foundational challenges. This talk revisits the “blind spots” of state-of-the-art vision systems, focusing on problems that remain difficult in real-world applications. I will share insights from recent work on multi-object tracking—specifically cases involving prolonged occlusions, identity switches, and visually indistinguishable subjects such as identical triplets in motion. Through examples from DragonTrack and other mehtods, I’ll explore why these problems persist and what they reveal about the current limits of our models. Ultimately, this talk invites us to look beyond benchmark scores and rethink how we define progress in visual perception.

About the Speaker

Bishoy Galoaa is an incoming PhD student in Electrical and Computer Engineering at Northeastern University, under the supervision of Prof. Sarah Ostadabbas. His research centers on multi-object tracking and scene understanding in complex environments, with a focus on problems that challenge the assumptions of current deep learning models.

LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living

Current Large Language Vision Models trained on web videos perform well in general video understanding but struggle with fine-grained details, complex human-object interactions (HOI), and view-invariant representation learning essential for Activities of Daily Living (ADL). In this talk, I will introduce a foundation model: LLAVIDAL catered towards understanding ADL and the tricks to train such models.

Paper: LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living

About the Speaker

Srijan Das is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Charlotte. At UNC Charlotte, he is working on Video Representation Learning, and Robotic Vision. He is a member of the AI4Health Center and one of the founding members of the Charlotte Machine Learning Lab (CharMLab) at UNC Charlotte.