Image instance segmentation based on diffusion model improved by step noisy.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Image instance segmentation is vital for autonomous driving and medical imaging.
• Diffusion models are state-of-the-art for image-related research, initially excelling in image enhancement.

Purpose of the Study:

• To improve the recognition accuracy of diffusion models in image instance segmentation tasks.
• To introduce and evaluate a novel algorithm for enhancing diffusion model performance in segmentation.

Main Methods:

• Proposes a Step Noisy Perception (SNP) method for diffusion models.
• Leverages controllable relationships between different Gaussian distributions and step sizes during inverse denoising.
• Utilizes inter-stage information from varying step sizes to boost segmentation accuracy.

Main Results:

• The SNP method demonstrates superior performance in recognizing small and medium-sized objects.
• The proposed model shows competitive results for large object recognition.
• Achieved a 2.8% accuracy improvement compared to standard diffusion model training.

Conclusions:

• The Step Noisy Perception method offers a valuable advancement for diffusion models in image instance segmentation.
• The improved diffusion model exhibits significant research potential for enhancing segmentation tasks.
• The method shows particular strength in accurately segmenting objects of various sizes.