In recent years, there has been a growing trend toward training data-centric, large-scale foundation models that reduce reliance on structural priors. However, is simply scaling up Transformers truly the ultimate solution for computer vision? In this workshop, we aim to reintroduce structural priors and explore how they can further push the boundaries of foundation models.

Our workshop provides an interdisciplinary space for sharing ideas across domains. For example, scene-aware 2D perception can enhance 3D modeling and robotic manipulation, while geometric reasoning can enhance the visual grounding of 2D perception and multimodal models. Through these interactions, we aim to better define the role of priors in vision foundation models.

Our topics include but are not limited to:

• Scene-aware vision models for images and videos.
• Geometry and equivariance for 3D vision.
• Temporal and motion priors for videos.
• Behavioral priors for robotics and egocentric views.
• Physics priors for world models and interactions.