Akash Pal

Hyperbolic Prototype Learning with Uncertainty-Aware Consistency for Continual Test-Time Segmentation

Continual Test-Time Adaptation (CTTA) for semantic segmentation is vital for deploying vision models in dynamic environments with persistent domain shifts. Existing methods often degrade over time as self-supervised updates amplify early prediction errors. We attribute this fragility to a geometric