July 17 - AI, ML and Computer Vision Meetup
Details
When and Where
July 17, 2025 | 10:00 – 11:30 AM Pacific
Using VLMs to Navigate the Sea of Data
At SEA.AI, we aim to make ocean navigation safer by enhancing situational awareness with AI. To develop our technology, we process huge amounts of maritime video from onboard cameras. In this talk, we’ll show how we use Vision-Language Models (VLMs) to streamline our data workflows; from semantic search using embeddings to automatically surfacing rare or high-interest events like whale spouts or drifting containers. The goal: smarter data curation with minimal manual effort.
About the Speaker
Daniel Fortunato, an AI Researcher at SEA.AI, is dedicated to enhancing efficiency through data workflow optimizations. Daniel’s background includes a Master’s degree in Electrical Engineering, providing a robust framework for developing innovative AI solutions. Beyond the lab, he is an enthusiastic amateur padel player and surfer.
SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation
Referring Video Object Segmentation (RVOS) involves segmenting objects in video based on natural language descriptions. SAMWISE builds on Segment Anything 2 (SAM2) to support RVOS in streaming settings, without fine-tuning and without relying on external large Vision-Language Models. We introduce a novel adapter that injects temporal cues and multi-modal reasoning directly into the feature extraction process, enabling both language understanding and motion modeling. We also unveil a phenomenon we denote tracking bias, where SAM2 may persistently follow an object that only loosely matches the query, and propose a learnable module to mitigate it. SAMWISE achieves state-of-the-art performance across multiple benchmarks with less than 5M additional parameters.
About the Speaker
Claudia Cuttano is a PhD student at Politecnico di Torino (VANDAL Lab), currently on a research visit at TU Darmstadt, where she works with Prof. Stefan Roth in the Visual Inference Lab. Her research focuses on semantic segmentation, with particular emphasis on multi-modal understanding and the use of foundation models for pixel-level tasks.
Building Efficient and Reliable Workflows for Object Detection
Training complex AI models at scale requires orchestrating multiple steps into a reproducible workflow and understanding how to optimize resource utilization for efficient pipelines. Modern MLOps practices help streamline these processes, improving the efficiency and reliability of your AI pipelines.
About the Speaker
Sage Elliott is an AI Engineer with a background in computer vision, LLM evaluation, MLOps, IoT, and Robotics. He’s taught thousands of people at live workshops. You can usually find him in Seattle biking around to parks or reading in cafes, catching up on the latest read for AI Book Club.
Your Data Is Lying to You: How Semantic Search Helps You Find the Truth in Visual Datasets
High-performing models start with high-quality data—but finding noisy, mislabeled, or edge-case samples across massive datasets remains a significant bottleneck. In this session, we’ll explore a scalable approach to curating and refining large-scale visual datasets using semantic search powered by transformer-based embeddings. By leveraging similarity search and multimodal representation learning, you’ll learn to surface hidden patterns, detect inconsistencies, and uncover edge cases. We’ll also discuss how these techniques can be integrated into data lakes and large-scale pipelines to streamline model debugging, dataset optimization, and the development of more robust foundation models in computer vision. Join us to discover how semantic search reshapes how we build and refine AI systems.
About the Speaker
Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.
Sponsors
July 17 - AI, ML and Computer Vision Meetup