Hierarchical Prototype Learning for Semantic Segmentation

Abstract

Conventional semantic segmentation methods often fail to distinguish fine-grained parts within the same object because of missing links between part-level cues and object-level semantics. Inspired by how humans recognize objects, which involves first identifying them as a whole and then distinguishing their parts, we propose a hierarchical prototype-based segmentation method called Hierarchical Prototype Segmentation (HiPoSeg). This builds a structured prototype space that captures both abstract object-level representations and detailed part-level features, enabling consistent alignment between levels. HiPoSeg leverages a hierarchical contrastive learning strategy to structure semantic representations across levels, encouraging both intra-level discrimination and cross-level consistency. Experiments on standard benchmarks such as Cityscapes, ADE20K, Mapillary Vistas 2.0, and PASCAL-Part-108 demonstrate that HiPoSeg produces consistent performance improvement with an average gain of +3.07\%p mIoU without any additional inference cost.