Parameter-Efficient Modality-Balanced Symmetric Fusion for Multimodal Remote Sensing Semantic Segmentation
Haocheng Li, Juepeng Zheng, Shuangxi Miao, Ruibo Lu, Guosheng Cai, Haohuan Fu, Jianxi Huang

TL;DR
MoBaNet is a parameter-efficient, modality-balanced fusion framework for multimodal remote sensing segmentation that leverages a largely frozen foundation model, innovative modules, and training strategies to improve performance and robustness.
Contribution
It introduces a novel symmetric dual-stream architecture with modules for deep semantic interaction, adaptive fusion, and modality imbalance mitigation, all designed for efficient fine-tuning.
Findings
Achieves state-of-the-art results on ISPRS benchmarks.
Uses significantly fewer trainable parameters than full fine-tuning.
Demonstrates robustness and balanced multimodal fusion.
Abstract
Multimodal remote sensing semantic segmentation enhances scene interpretation by exploiting complementary physical cues from heterogeneous data. Although pretrained Vision Foundation Models (VFMs) provide strong general-purpose representations, adapting them to multimodal tasks often incurs substantial computational overhead and is prone to modality imbalance, where the contribution of auxiliary modalities is suppressed during optimization. To address these challenges, we propose MoBaNet, a parameter-efficient and modality-balanced symmetric fusion framework. Built upon a largely frozen VFM backbone, MoBaNet adopts a symmetric dual-stream architecture to preserve generalizable representations while minimizing the number of trainable parameters. Specifically, we design a Cross-modal Prompt-Injected Adapter (CPIA) to enable deep semantic interaction by generating shared prompts and…
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Taxonomy
TopicsAdvanced Neural Network Applications · Remote-Sensing Image Classification · Domain Adaptation and Few-Shot Learning
