MAFS: Masked Autoencoder for Infrared-Visible Image Fusion and Semantic Segmentation

• URL: http://arxiv.org/abs/2509.11817v1
• Date: Mon, 15 Sep 2025 11:55:55 GMT
• Title: MAFS: Masked Autoencoder for Infrared-Visible Image Fusion and Semantic Segmentation
• Authors: Liying Wang, Xiaoli Zhang, Chuanmin Jia, Siwei Ma,
• Abstract summary: We propose a unified network for image fusion and semantic segmentation.<n>We devise a heterogeneous feature fusion strategy to enhance semantic-aware capabilities for image fusion.<n>Within the framework, we design a novel multi-stage Transformer decoder to aggregate fine-grained multi-scale fused features efficiently.
• Score: 43.62940654606311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared-visible image fusion methods aim at generating fused images with good visual quality and also facilitate the performance of high-level tasks. Indeed, existing semantic-driven methods have considered semantic information injection for downstream applications. However, none of them investigates the potential for reciprocal promotion between pixel-wise image fusion and cross-modal feature fusion perception tasks from a macroscopic task-level perspective. To address this limitation, we propose a unified network for image fusion and semantic segmentation. MAFS is a parallel structure, containing a fusion sub-network and a segmentation sub-network. On the one hand, We devise a heterogeneous feature fusion strategy to enhance semantic-aware capabilities for image fusion. On the other hand, by cascading the fusion sub-network and a segmentation backbone, segmentation-related knowledge is transferred to promote feature-level fusion-based segmentation. Within the framework, we design a novel multi-stage Transformer decoder to aggregate fine-grained multi-scale fused features efficiently. Additionally, a dynamic factor based on the max-min fairness allocation principle is introduced to generate adaptive weights of two tasks and guarantee smooth training in a multi-task manner. Extensive experiments demonstrate that our approach achieves competitive results compared with state-of-the-art methods. The code is available at https://github.com/Abraham-Einstein/MAFS/.

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