14 citations · 32 across the 21 of their papers we have counts for
5 papers · 1 filter
What Drives Compositional Generalization in Visual Generative Models? The Importance of Continuous Training Objectives
Karim Farid, Rajat Sahay, Yumna Ali Alnaggar +4
Compositional generalization, the ability to generate novel combinations of known concepts, is a key ingredient for visual generative models. Yet, not all mechanisms that enable or…
Towards Understanding Subliminal Learning: When and How Hidden Biases Transfer
Simon Schrodi, Elias Kempf, Fazl Barez +1
Language models can transfer hidden biases during distillation. For example, a teacher that "likes owls" can make its student "like owls" too, even when the training data consists…
Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models
Arian Mousakhan, Sudhanshu Mittal, Silvio Galesso +2
Existing world models for autonomous driving struggle with long-horizon generation and generalization to challenging scenarios. In this work, we develop a model using simple design…
cVLA: Towards Efficient Camera-Space VLAs
Max Argus, Jelena Bratulic, Houman Masnavi +4
Vision-Language-Action (VLA) models offer a compelling framework for tackling complex robotic manipulation tasks, but they are often expensive to train. In this paper, we propose a…
When and How Does CLIP Enable Domain and Compositional Generalization?
Elias Kempf, Simon Schrodi, Max Argus +1
The remarkable generalization performance of contrastive vision-language models like CLIP is often attributed to the diversity of their training distributions. However, key questio…