Knowledge-Guided Semantic Transfer Network for Few-Shot Image Recognition.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Deep learning excels with large datasets but struggles with novel categories.
• Few-shot learning aims to enable image recognition from minimal examples.
• Human learning relies on prior visual and semantic knowledge for rapid concept acquisition.

Purpose of the Study:

• To propose a novel knowledge-guided semantic transfer network (KSTNet) for few-shot image recognition.
• To integrate vision inferring, knowledge transferring, and classifier learning into a unified framework.
• To leverage auxiliary prior knowledge for improved few-shot learning performance.

Main Methods:

• Developed a category-guided visual learning module using feature extraction, cosine similarity, and contrastive loss.
• Implemented a knowledge transfer network to learn semantic-visual mappings and infer classifiers for novel categories.
• Designed an adaptive fusion scheme to combine visual and knowledge-based information for final classifier inference.

Main Results:

• KSTNet demonstrated effectiveness on Mini-ImageNet and Tiered-ImageNet benchmarks.
• The proposed method achieved favorable performance compared to state-of-the-art approaches.
• Significant improvements were observed, particularly in one-shot learning scenarios.

Conclusions:

• KSTNet successfully incorporates prior knowledge to enhance few-shot image recognition.
• The unified framework offers an effective approach for learning from limited labeled examples.
• The method shows promise for real-world applications requiring rapid adaptation to new visual categories.