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20242026
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cs.CV2026

Visual prompt engineering for video models

Robert Geirhos, Yuxuan Li, Thaddäus Wiedemer +7

In the age of foundation models, a model is only as good as its prompt. For this reason, prompt engineering has become an essential technique for improving language model performan…

cs.CV2026

Physics-IQ Verified

Tim Rädsch, Yuki M Asano, Hilde Kuehne +4

Video generative models ( VGMs) have become a new frontier that can be used not just for video generation but for a multitude of downstream tasks, including world modeling. To adva…

cs.CV2025

Towards flexible perception with visual memory

Robert Geirhos, Priyank Jaini, Austin Stone +5

Training a neural network is a monolithic endeavor, akin to carving knowledge into stone: once the process is completed, editing the knowledge in a network is hard, since all infor…

cs.CV2025

Do generative video models understand physical principles?

Saman Motamed, Laura Culp, Kevin Swersky +2

AI video generation is undergoing a revolution, with quality and realism advancing rapidly. These advances have led to a passionate scientific debate: Do video models learn "world…

cs.CV2024

Decoupling Semantic Similarity from Spatial Alignment for Neural Networks

Tassilo Wald, Constantin Ulrich, Gregor Köhler +6

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions…

cs.CV2024

Intriguing properties of generative classifiers

Priyank Jaini, Kevin Clark, Robert Geirhos

What is the best paradigm to recognize objects -- discriminative inference (fast but potentially prone to shortcut learning) or using a generative model (slow but potentially more…