Phase Recognition

Phase recognition, the automated identification of distinct stages or phases within a dynamic process, is a rapidly developing field with applications across diverse domains. Current research focuses on leveraging deep learning, particularly convolutional neural networks (CNNs) and transformers, often incorporating techniques like masked video distillation and temporal modeling to improve accuracy and robustness, especially in handling noisy or incomplete data. These advancements are significantly impacting fields like surgical workflow analysis, where automated phase recognition aids in skill assessment and training, and medical imaging, enabling more efficient and accurate diagnosis. Furthermore, unsupervised and training-free methods are being explored to reduce reliance on large, manually annotated datasets.