HCFormer: Unified Image Segmentation with Hierarchical Clustering

Understand

Hierarchical clustering is an effective and efficient approach widely used for classical image segmentation methods.

• However, many existing methods using neural networks generate segmentation masks directly from per-pixel features, complicating the architecture design and degrading the interpretability.
• In this work, we propose a simpler, more interpretable architecture, called HCFormer.
• HCFormer accomplishes image segmentation by bottom-up hierarchical clustering and allows us to interpret, visualize, and evaluate the intermediate results as hierarchical clustering results.