Test Time Optimization

Test-time optimization (TTO) refines pre-trained models during the testing phase to improve performance on specific inputs, addressing limitations of standard training approaches like generalization issues and distribution shifts. Current research focuses on adapting TTO to various tasks, including image and video restoration, human pose estimation, and language model evaluation, often leveraging techniques like uncertainty quantification, self-supervised learning, and meta-learning to enhance model robustness and efficiency. This approach holds significant promise for improving the accuracy and reliability of AI systems in real-world applications where data variability is common, particularly in resource-constrained environments or time-critical scenarios like medical image analysis.