Frozen is better than learning: A new design of prototype-based classifier for semantic segmentation

Abstract

Semantic segmentation models comprise an encoder to extract features and a classifier for prediction. However, the learning of the classifier suffers from the ambiguity which is caused by two factors: (1) the weights of a classifier for similar categories may have positive similarities lowing the performance for similar categories, named correlation ambiguity, and (2) the classifier is prone to predict the category with a larger ℓ2 norm and vice versa, termed prior ambiguity. To comedy the issues, we propose Category-Basis Prototype (CBP), frozen and mutually orthogonalized prototypes with equalℓ2norm. Orthogonalization prevents the prototypes from being similar to each other and the equality decouples the prediction from the ℓ2 norm. To better shape the feature space, we propose Online Centroid Contrastive Loss (OCCL) equipped with centroid and category-level losses. Experiments show that our method yields compelling results over two widely applied benchmarks indicating the effectiveness of our methods.