New AI Model H-CAST Enhances Image Classification with Hierarchical Understanding

Researchers at the University of Michigan have introduced H-CAST, an innovative AI model that classifies images by creating a hierarchical tree—from broad categories like “bird” to very specific ones such as “bald eagle.” Unlike previous models that treat coarse and fine classifications as separate tasks, H-CAST aligns these levels through intra-image segmentation, focusing attention on the same object at multiple detail layers. This leads to more accurate and consistent predictions. Traditional models often struggle with imperfect or unclear images, but H-CAST’s hierarchical approach enables it to provide reliable classifications at varying levels of detail. Tested on four benchmark datasets, H-CAST outperformed existing state-of-the-art models by up to 11% in accuracy, while also reducing inconsistencies between coarse and fine predictions.

This technology shows strong potential for real-world applications such as wildlife monitoring—where it can identify species when possible or fallback to broader categories—and autonomous vehicles, which can use coarser predictions to safely interpret occluded or distant objects. By mimicking human flexible reasoning, H-CAST marks a significant advancement in building AI systems that are both more interpretable and robust in complex visual environments.