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Research#Computer Vision📝 BlogAnalyzed: Dec 29, 2025 07:56

Trends in Computer Vision with Pavan Turaga - #444

Published:Jan 4, 2021 22:33
1 min read
Practical AI

Analysis

This article from Practical AI discusses trends in computer vision, featuring an interview with Pavan Turaga, an Associate Professor at Arizona State University. The focus is on the evolution of computer vision in the past year, including the resurgence of physics-based scene understanding and differential rendering. The article also highlights key research papers and future directions. The call to action encourages audience participation through comments and social media, fostering engagement with the discussed topics.
Reference

“We explore the revival of physics-based thinking about scenes, differential rendering, the best papers, and where the field is going in the near future.”

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Research#Computer Vision📝 BlogAnalyzed: Dec 29, 2025 08:02

Invariance, Geometry and Deep Neural Networks with Pavan Turaga - #386

Published:Jun 25, 2020 17:08
1 min read
Practical AI

Analysis

This article summarizes a discussion with Pavan Turaga, an Associate Professor at Arizona State University, focusing on his research integrating physics-based principles into computer vision. The conversation likely revolved around his keynote presentation at the Differential Geometry in CV and ML Workshop, specifically his work on revisiting invariants using geometry and deep learning. The article also mentions the context of the term "invariant" and its relation to Hinton's Capsule Networks, suggesting a discussion on how to make deep learning models more robust to variations in input data. The focus is on the intersection of geometry, physics, and deep learning within the field of computer vision.
Reference

“The article doesn't contain a direct quote, but it likely discussed the integration of physics-based principles into computer vision and the concept of "invariant" in relation to deep learning.”

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