41 citations · 186 across the 21 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…
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…
Text-to-Image Diffusion Models are Zero-Shot Classifiers
Kevin Clark, Priyank Jaini
The excellent generative capabilities of text-to-image diffusion models suggest they learn informative representations of image-text data. However, what knowledge their representat…