Prior Guided Feature Enrichment Network for Few-Shot Segmentation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• State-of-the-art semantic segmentation models demand extensive labeled data and struggle with unseen classes without fine-tuning.
• Few-shot segmentation aims to enable models to adapt to new classes using limited labeled support samples.
• Existing few-shot methods face reduced generalization due to improper use of high-level semantic information and spatial inconsistencies.

Purpose of the Study:

• To propose the Prior Guided Feature Enrichment Network (PFENet) to address the limitations of current few-shot segmentation methods.
• To enhance the generalization ability and performance of models on unseen classes in semantic segmentation tasks.

Main Methods:

• Developed a training-free prior mask generation method to retain generalization power and improve model performance.
• Introduced a Feature Enrichment Module (FEM) to overcome spatial inconsistency by adaptively enriching query features with support features and prior masks.
• Evaluated the proposed methods on PASCAL-5 i and COCO datasets.

Main Results:

• The prior generation method and FEM significantly improved the baseline method's performance.
• PFENet outperformed state-of-the-art methods by a large margin without compromising efficiency.
• The model demonstrated surprising generalization capabilities, even in scenarios lacking labeled support samples.

Conclusions:

• The proposed PFENet effectively enhances few-shot semantic segmentation by leveraging prior masks and feature enrichment.
• PFENet offers a robust solution for generalizing to unseen classes, surpassing existing methods.
• The approach shows potential for few-shot learning applications where labeled data is scarce.