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A Self-Supervised Few-Shot Semantic Segmentation Method Based on Multi-Task Learning and Dense Attention Computation.

Kai Yi¹, Weihang Wang², Yi Zhang²

¹Intelligent Policing Key Laboratory of Sichuan Province, Luzhou 646099, China.

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

This summary is machine-generated.

This study introduces a self-supervised few-shot semantic segmentation method (MLDAC) for autonomous driving. MLDAC significantly reduces manual annotation needs for intelligent vehicle perception systems.

Keywords:

Swin Transformer few-shot semantic segmentation multi-task learning scene understanding self-supervised learning

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Area of Science:

Computer Vision
Artificial Intelligence
Autonomous Systems

Background:

Intelligent vehicles rely on vision systems for scene understanding, utilizing semantic segmentation.
Traditional supervised semantic segmentation requires extensive pixel-level manual annotations, which is labor-intensive.
Few-shot methods reduce annotation but remain demanding.

Purpose of the Study:

To propose a novel self-supervised few-shot semantic segmentation method (MLDAC).
To reduce the reliance on manual annotations in training semantic segmentation models for autonomous driving.
To enhance the generalization ability and efficiency of perception systems.

Main Methods:

Developed a self-supervised few-shot semantic segmentation approach using Multi-task Learning and Dense Attention Computation (MLDAC).
Employed Swin Transformer as a backbone for multi-scale feature extraction.
Integrated dense attention computation blocks and inter-scale mixing with feature skip connections.

Main Results:

Achieved 55.1% and 26.8% one-shot mIoU on PASCAL-5i and COCO-20i datasets, respectively.
Demonstrated strong performance with 78.1% accuracy on the FSS-1000 dataset.
Validated the efficacy of the proposed MLDAC method in self-supervised few-shot segmentation tasks.

Conclusions:

MLDAC effectively reduces annotation burden for semantic segmentation in autonomous driving.
The method shows significant improvements in few-shot segmentation accuracy and generalization.
MLDAC offers a promising direction for efficient scene understanding in intelligent vehicles.