Rethinking Semantic Segmentation With Multi-Grained Logical Prototype

Abstract

The last decade has witnessed significant advances in semantic segmentation brought about by deep learning. However, existing methods only fit the data-label correspondence in a data-driven manner and do not fully conform to the abstraction and structuralization characteristics of the human visual cognition process, which limits the upper bounds of their performance. To this end, a multi-grained logical prototype (MGLP) method is proposed to rethink semantic segmentation based on these two key characteristics. Its novel design can be summarized as follows. 1) For abstraction, prototypes of the same class at different grain levels are established: a label generation method is proposed to automatically generate a multi-grained label space, which can guide the learning of the multi-grained prototypes for each class. 2) For structuralization, the intrinsic logical structure across different semantic levels is explicitly modeled: the horizontal metric relationships are established via metric relation operations on prototypes at the same grain level, to improve the discriminability between classes while taking the vertical semantic hierarchy into account. Moveover, the vertical logical relationships are established as the sub-to-super positive and super-to-sub negative constraints, to strengthen the semantic dependencies among prototypes at different grain levels. 3)MGLP is plug-and-play and can be directly combined with existing segmentation methods. Extensive experimental results indicate that MGLP can significantly improve the segmentation performance of existing methods, which opens up a new avenue for future research.