MSSSeg: Learning Multi-Scale Structural Complexity for Self-Supervised Segmentation

ArXi:2512.23997v2 Announce Type: replace Self-supervised semantic segmentation methods often suffer from structural errors, including merging distinct objects or fragmenting coherent regions, because they rely primarily on low-level appearance cues such as color and texture. These cues lack structural discriminability: they carry no information about the structural organization of a region, making it difficult to distinguish boundaries between similar-looking objects or maintain coherence within internally varying regions.