Composite Object Relation Modeling for Few-Shot Scene Recognition.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Few-shot image recognition typically uses Convolutional Neural Networks (CNNs) for global feature learning, which struggles with complex scene images due to diverse object relations.
• Existing methods are less effective for abstract and complex scene images where spatial relationships between numerous objects are crucial.

Purpose of the Study:

• To propose a composite object relation modeling method for few-shot scene recognition.
• To enhance the adaptability of models to novel scenes by capturing spatial structural characteristics.
• To address limitations of global feature learning in complex scene recognition tasks.

Main Methods:

• Developed a task-aware region selection module (TRSM) to select relevant object regions dynamically for different tasks.
• Constructed image representations using graphs that model objects and their spatial relations.
• Employed graph convolutional networks (GCNs) to model these graph-based representations.
• Jointly optimized graph modeling with few-shot recognition loss for adaptive representation learning.

Main Results:

• The proposed graph-based representation effectively captures spatial structural characteristics of scene images.
• The method demonstrates enhanced adaptability to novel scenes compared to traditional approaches.
• Experimental results confirm the effectiveness of the composite object relation modeling for few-shot scene recognition.

Conclusions:

• Modeling object relations offers a more robust approach for few-shot scene recognition than relying solely on global features.
• The proposed graph-based method, incorporating task-aware region selection, significantly improves performance on complex scene recognition tasks.
• The approach is versatile and can be integrated into various few-shot learning architectures.