Year: 2025
Type: conference-paper
Abstract: Domain Generalized Semantic Segmentation (DGSS) aims to utilize segmentation model training on known source domains to make predictions on unknown target domains. Currently, there are two network architectures: one based on Convolutional Neural Networks (CNNs) and the other based on Visual Transformers (ViTs). However, both CNN-based and ViT-based DGSS methods face challenges: the former lacks a global receptive field, while the latter requires more computational demands. Drawing inspiration from State Space Models (SSMs), which not only possess a global receptive field but also maintain linear complexity, we propose SSM-based method for achieving DGSS. In this work, we first elucidate why does mask make sense in SSM-based DGSS and propose our mask learning mechanism. Leveraging this mechanism, we present our Mask Vision Mamba network (MaskViM), a model for SSM-based DGSS, and design our mask loss to optimize MaskViM. Our method achieves superior performance on four diverse DGSS setting, which demonstrates the effectiveness of our method.
Authors: Jiahao Li, Yang Lu, Yuan Xie, Yanyun Qu
Institutions: Xiamen University, East China Normal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409367474