Channel2DTransformer: A Multi-level Features Self-attention Fusion Module for Semantic Segmentation

Abstract

Semantic segmentation is a crucial technology for intelligent vehicles, enabling scene understanding in complex driving environments. However, complex real-world scenarios often contain diverse multi-scale objects, which bring challenges to the accurate semantic segmentation. To address this challenge, we propose a multi-level features self-attention fusion module called Channel2DTransformer. The module utilizes self-attention mechanisms to dynamically fuse multi-level features by computing self-attention weights between their channels, resulting in a consistent and comprehensive representation of scene features. We perform the module on the Cityscapes and NYUDepthV2 datasets, which contain a large number of multi-scale objects. The experimental results validate the positive contributions of the module in enhancing the semantic segmentation accuracy of multi-scale objects and improving the performance of semantic segmentation in complex scenes.