What is hierarchical vision modeling and why is it important for dense prediction tasks?

Hierarchical vision models progressively reduce spatial resolution while increasing semantic abstraction.

Hierarchical models (like ResNet, FPN, Swin Transformer) build multi-level feature representations. Early layers retain high-resolution spatial detail, while deeper layers encode semantic context. This structure is essential for tasks like detection and segmentation where both localization and semantics matter. Unlike vanilla ViTs, hierarchical designs improve efficiency and inductive bias.