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Updated: May 31, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Efficient Multi-Task Training with Adaptive Feature Alignment for Universal Image Segmentation.

Yipeng Qu¹, Joohee Kim¹

¹Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This summary is machine-generated.

This study introduces a novel Adaptive Feature Alignment (AFA) method with a learnable task token for universal image segmentation. This approach effectively captures task differences, improving model efficiency and performance over text-based tokens.

Keywords:

computer vision feature alignment multimodal learning universal image segmentation

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Universal image segmentation models aim for single architecture, multi-task training.
Current methods use text-based task tokens, which lack inherent task differentiation and cause modality discrepancies.
Existing alignment methods are computationally expensive and unsuitable for resource-constrained devices.

Purpose of the Study:

To propose an Adaptive Feature Alignment (AFA) method with a learnable task token for universal image segmentation.
To address limitations of text-based task tokens and complex modality alignment methods.
To enhance efficiency and effectiveness in lightweight segmentation models.

Main Methods:

Introduced a learnable task token that automatically captures inter-task differences from image features and text queries.
Developed Adaptive Feature Alignment (AFA) by replacing image features with class-specific means for efficient cross-modal alignment.
Integrated the learnable task token with AFA for unified multi-task training.

Main Results:

The proposed model with AFA and learnable task token demonstrated superior efficiency and effectiveness compared to baseline models.
Achieved state-of-the-art performance on ADE20K and Cityscapes datasets with comparable parameter counts.
Showcased the advantage of learnable task tokens over predefined text-based tokens in capturing task nuances.

Conclusions:

The AFA method with a learnable task token offers a more effective and efficient solution for universal image segmentation.
This approach overcomes the limitations of text-based conditioning and complex alignment strategies.
Enables high-performance segmentation on resource-constrained devices.