Aggregation of multi-stage features has been revealed to play a significant
role in semantic segmentation. Unlike previous methods employing point-wise
summation or concatenation for feature aggregation, this study proposes the
Category Feature Transformer (CFT) that explores the flow of category embedding
and transformation among multi-stage features through the prevalent multi-head
attention mechanism. CFT learns unified feature embeddings for individual
semantic categories from high-level features during each aggregation process
and dynamically broadcasts them to high-resolution features. Integrating the
proposed CFT into a typical feature pyramid structure exhibits superior
performance over a broad range of backbone networks. We conduct extensive
experiments on popular semantic segmentation benchmarks. Specifically, the
proposed CFT obtains a compelling 55.1% mIoU with greatly reduced model
parameters and computations on the challenging ADE20K dataset.

利用类别特征转换器（CFT）来探索多阶段特征之间的类别嵌入和转换，通过常见的多头注意力机制学习统一的特征嵌入，并将其动态广播到高分辨率特征，将 CFT 集成到典型的特征金字塔结构中可以在多种骨干网络上展现卓越性能，经过在流行的语义分割基准数据集上的大规模实验，CFT 在具有挑战性的 ADE20K 数据集上获得了引人注目的 55.1％mIoU，同时大大减少了模型参数和计算量。