Camera Lens
Camera lenses, in their broadest sense, are systems for focusing light to form images, impacting diverse fields from autonomous driving to artistic expression and even large language model (LLM) development. Current research focuses on improving image quality through computational aberration correction and depth-of-field control, often employing novel architectures like diffusion models and leveraging techniques from Riemannian geometry and network flows for enhanced performance. These advancements are crucial for improving the accuracy and reliability of computer vision systems, enabling new applications in various domains, and furthering our understanding of complex data representations.
Papers
Projection Abstractions in Planning Under the Lenses of Abstractions for MDPs
Giuseppe Canonaco, Alberto Pozanco, Daniel Borrajo
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis, Daniel Rueckert, Gintare Karolina Dziugaite, Eleni Triantafillou
Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
Declan Campbell, Sunayana Rane, Tyler Giallanza, Nicolò De Sabbata, Kia Ghods, Amogh Joshi, Alexander Ku, Steven M. Frankland, Thomas L. Griffiths, Jonathan D. Cohen, Taylor W. Webb
Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving
Ce Zhou (1), Qiben Yan (1), Daniel Kent (1), Guangjing Wang (2), Weikang Ding (1), Ziqi Zhang (3), Hayder Radha (1) ((1) Michigan State University, (2) University of South Florida, (3) Peking University)
Dense Associative Memory Through the Lens of Random Features
Benjamin Hoover, Duen Horng Chau, Hendrik Strobelt, Parikshit Ram, Dmitry Krotov