Single Image Test Time Adaptation

Single image test-time adaptation (TTA) focuses on improving the performance of pre-trained deep learning models on unseen data without retraining or access to labeled target data, adapting solely based on a single test image. Current research emphasizes efficient algorithms, often leveraging techniques like contrastive learning, self-supervised learning, and style transfer, sometimes integrated with normalization layer adjustments (e.g., Batch Normalization) to handle the limited information available. This area is significant because it addresses the real-world challenge of deploying models in dynamic environments with limited resources, improving robustness and applicability across diverse domains like medical imaging, object detection, and robotics.