Join the Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

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.

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.

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.

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.