ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models

Researchers propose ProtoDCS, a new framework for robust test-time adaptation of Vision-Language Models in open-set scenarios. The method uses Gaussian Mixture Model verification and uncertainty-aware learning to better handle distribution shifts while maintaining computational efficiency.