Hierarchical Prototype Learning for Semantic Segmentation

ICLR 2026 Conference Submission17423 Authors

Keywords: Semantic Segmentation, Contrastive Learning, Prototypical Networks

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). HiPoSeg builds a structured prototype space that captures both abstract object-level representations and detailed part-level features, enabling consistent alignment between levels. The model 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 including Cityscapes, ADE20K, Mapillary Vistas 2.0, and PASCAL-Part-108 demonstrate that HiPoSeg produces consistent improvement of performance with an average +3.07\%p mIoU gain without any additional inference cost.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Submission Number: 17423