Contrastive Ordinal

Contrastive ordinal methods enhance regression models by incorporating ordinal relationships between target values, improving performance in tasks where data exhibits inherent order. Current research focuses on developing novel loss functions, such as supervised contrastive ordinal loss and ordinal entropy loss, and integrating contrastive learning techniques to improve feature representation and inter-class separability. These advancements are applied across diverse fields, including automatic pronunciation assessment, medical image analysis (e.g., abdominal aortic calcification scoring), and computer vision tasks like depth estimation, demonstrating the broad applicability and impact of this approach. The resulting improvements in accuracy and robustness are significant for various applications requiring precise ordinal predictions.