{\Phi}eat: Physically-Grounded Feature Representation

The paper titled "\Phi eat: Physically-Grounded Feature Representation" introduces a new visual backbone designed to enhance self-supervised learning in vision tasks. Current self-supervised features often mix high-level semantics with low-level physical factors, which can limit their effectiveness in tasks requiring physical reasoning. The proposed \Phi eat model focuses on material identity and employs a pretraining strategy that contrasts spatial crops and physical augmentations of materials under various conditions, aiming to improve feature robustness.