When

January 25, 2024 – 10:00 AM Pacific

Where

Virtual / Zoom

Agenda

• Lightning Talk: Next-Generation Image/Video editor Built with Generative AI – Mihail Eric at Storia AI
  
• SANPO: A Scene Understanding, Accessibility, Navigation, Pathfinding, Obstacle Avoidance Dataset – Kimberly Wilber at Google Research
• Setmlvis: Object Detection Comparison Using Set Visualization – Liudas Panavas at Northeastern University 
• How well does the Segment Anything Model work on a Fisheye lens? – Nahid Alam at Cisco Meraki

SANPO: A Scene Understanding, Accessibility, Navigation, Pathfinding, Obstacle Avoidance Dataset

In this talk we introduce SANPO, a large-scale egocentric video dataset focused on dense prediction in outdoor environments. It contains stereo video sessions collected across diverse outdoor environments, as well as rendered synthetic video sessions. To our knowledge, this is the first human egocentric video dataset with both large scale dense panoptic segmentation and depth annotations.

Speaker: Kimberly Wilber is a computer vision researcher at Google Research NYC. She previously studied tasks at the intersection of computer vision and crowdsourcing at Cornell Tech.

Setmlvis: Object Detection Comparison Using Set Visualization

We introduce Setmlvis, a novel tool employing set theory and visualization for object detection model comparison. It efficiently aggregates and matches detection data across multiple models, highlighting where models align or diverge in object detection. This approach allows for analysis of each model’s unique capabilities and common strengths. Through our system, we demonstrate how set theory and visualization can be used as a valuable asset in the model evaluation process.

Speaker: Liudas Panavas is a CS PhD student at Northeastern University’s Data Visualization Lab, where they focus on explainable AI for object detection algorithms. They specialize in developing visual analytics software and conducting comprehensive user experience evaluations.

How well does the Segment Anything Model work on a Fisheye lens?

The Segment Anything Model has revolutionized zero shot segmentation especially in rectilinear images. But how well does it work on fisheye lenses? These lenses produce wide panoramic or hemispherical images that have visual distortion specially at the edge of the image. In this talk, we will look into the inner workings of the Segment Anything Model, its usages and zero shot capabilities on fisheye images.

Speaker: Nahid Alam is a Staff ML Engineer working on video understanding on the Cisco Meraki Camera Intelligence team.

Lightning Talk: Next-Generation Image/Video editor Built with Generative AI

Storia Lab is a next-generation image/video editor built on the state-of-the-art in generative AI offering seamless visual capabilities including generation, style transfer, artifact cleanup, and more. Since launching in October, Storia Lab has already been used by tens of thousands of marketers, designers, and creatives to 100x their speed and quality of visual asset creation.

Speaker: Mihail Eric is a cofounder of Storia AI. He has over a decade of experience researching and engineering AI systems at scale. Previously he built the first deep learning dialogue systems at the Stanford NLP group. He was also a founding member of Amazon Alexa’s first special projects team where he built the organization’s earliest large language models.

Don’t Forget

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