The RGB-Thermal (RGB-T) information for semantic segmentation has been
extensively explored in recent years. However, most existing RGB-T semantic
segmentation usually compromises spatial resolution to achieve real-time
inference speed, which leads to poor performance. To better extract detail
spatial information, we propose a two-stage Feature-Enhanced Attention Network
(FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a
Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level
features from both the channel and spatial views. Benefited from the proposed
FEAM module, our FEANet can preserve the spatial information and shift more
attention to high-resolution features from the fused RGB-T images. Extensive
experiments on the urban scene dataset demonstrate that our FEANet outperforms
other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and
subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU).
For the 480 x 640 RGB-T test images, our FEANet can run with a real-time speed
on an NVIDIA GeForce RTX 2080 Ti card.

本文提出了一种双阶段特征增强注意力网络，用于 RGB-T 语义分割任务，可在保留空间信息的同时提高时间效率，并在城市场景数据集上优于其他 SOTA 的 RGB-T 方法。