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arXiv · 2308.05581

Category Feature Transformer for Semantic Segmentation

Quan Tang·Chuanjian Liu·Fagui Liu·Yifan Liu·Jun Jiang·Bowen Zhang·Kai Han·Yunhe Wang
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Abstract

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.

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Quan Tang, Chuanjian Liu, Fagui Liu, Yifan Liu, Jun Jiang, Bowen Zhang, Kai Han, Yunhe Wang. 2023-08-10. Category Feature Transformer for Semantic Segmentation. https://arxiv.org/abs/2308.05581

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