  Semantic segmentation, as a crucial component of complex visual
interpretation, plays a fundamental role in autonomous vehicle vision systems.
Recent studies have significantly improved the accuracy of semantic
segmentation by exploiting complementary information and developing multimodal
methods. Despite the gains in accuracy, multimodal semantic segmentation
methods suffer from high computational complexity and low inference speed.
Therefore, it is a challenging task to implement multimodal methods in driving
applications. To address this problem, we propose the Cosine Similarity Fusion
Network (CSFNet) as a real-time RGB-X semantic segmentation model.
Specifically, we design a Cosine Similarity Attention Fusion Module (CS-AFM)
that effectively rectifies and fuses features of two modalities. The CS-AFM
module leverages cross-modal similarity to achieve high generalization ability.
By enhancing the fusion of cross-modal features at lower levels, CS-AFM paves
the way for the use of a single-branch network at higher levels. Therefore, we
use dual and single-branch architectures in an encoder, along with an efficient
context module and a lightweight decoder for fast and accurate predictions. To
verify the effectiveness of CSFNet, we use the Cityscapes, MFNet, and ZJU
datasets for the RGB-D/T/P semantic segmentation. According to the results,
CSFNet has competitive accuracy with state-of-the-art methods while being
state-of-the-art in terms of speed among multimodal semantic segmentation
models. It also achieves high efficiency due to its low parameter count and
computational complexity. The source code for CSFNet will be available at
https://github.com/Danial-Qashqai/CSFNet.
