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LiteMFT: Lightweight Multi-Modal Fine-Tuning for Semantic Segmentation.

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IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 18, 2026

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This study presents LiteMFT, a lightweight framework for efficient multi-modal image segmentation using Vision Foundation Models (VFMs). LiteMFT significantly reduces computational costs while maintaining high performance across various segmentation tasks.

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Multi-modal image segmentation integrates data from diverse sensors for enhanced semantic predictions.
Growing data volumes and model capacities increase computational costs, especially with Vision Foundation Models (VFMs).
Existing methods struggle with parameter and computational efficiency in multi-modal segmentation.

Purpose of the Study:

To introduce a Lightweight Multi-modal Fine-Tuning (LiteMFT) framework for efficient adaptation of RGB-pretrained VFMs.
To enable generalizable multi-modal semantic segmentation with reduced parameters and computational overhead.
To address the challenges of scalability and efficiency in multi-modal image fusion tasks.

Main Methods:

Developed the LiteMFT framework with a small number of trainable parameters for efficient VFM adaptation.

Related Experiment Videos

Introduced the Modality Local Competition (MLC) module for dynamic and efficient cross-modal feature fusion.

Incorporated the Gated Low-Rank Adapter (GLR) for improved backbone adaptability via content-aware low-rank transformation.

Main Results:

LiteMFT demonstrated competitive or superior performance on bi-modal and tri-modal segmentation tasks.
The framework achieved significant reductions in parameters and computational costs compared to existing methods.
Experiments confirmed the strong scalability of LiteMFT for incorporating additional modalities.

Conclusions:

LiteMFT offers a practical and broadly applicable solution for efficient multi-modal semantic segmentation.
The framework effectively extends RGB-pretrained VFMs to multi-modal tasks without substantial increases in complexity.
LiteMFT provides a scalable approach for future advancements in multi-modal computer vision.