Exploiting Gaussian agnostic representation learning with diffusion priors for enhanced infrared small target...

Area of Science:

• Computer Science
• Artificial Intelligence
• Signal Processing

Background:

• Infrared small target detection (ISTD) is crucial for practical applications.
• Current ISTD methods rely on extensive manual labeling, leading to fragility in real-world scenarios with data scarcity.
• Existing theories on practical ISTD are challenged by performance variations under data scarcity.

Purpose of the Study:

• To investigate the performance limitations of mainstream ISTD methods under data scarcity.
• To develop a robust approach for representation learning in ISTD that addresses data limitations.
• To improve the quality and fidelity of synthetic infrared data for training ISTD models.

Main Methods:

• Gaussian Agnostic Representation Learning framework.
• Gaussian Group Squeezer utilizing Gaussian sampling and compression for non-uniform quantization.
• Two-stage diffusion models for real-world data reconstruction and synthetic sample generation.

Main Results:

• Enhanced resilience of ISTD models against various challenges through diverse training samples.
• Significantly improved quality and fidelity of synthetic samples by aligning quantized signals with real-world distributions.
• Demonstrated efficacy of the proposed approach against state-of-the-art methods in scarcity scenarios.

Conclusions:

• The proposed Gaussian Agnostic Representation Learning enhances ISTD model robustness in data-scarce environments.
• Two-stage diffusion models effectively generate high-fidelity synthetic data, mitigating real-world challenges.
• The approach offers a promising solution for improving the practical applicability of ISTD systems.