Explicit Margin Equilibrium for Few-Shot Object Detection.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Few-shot object detection (FSOD) faces challenges in transferring knowledge from base classes to novel classes with limited data.
• Existing methods struggle with the trade-off between class discrimination and representation within embedding spaces.
• Optimizing class margins is crucial for accommodating novel classes while maintaining accurate representations.

Purpose of the Study:

• To propose a novel class margin optimization scheme, Explicit Margin Equilibrium (EME), for few-shot object detection.
• To address the discrimination-representation dilemma by explicitly leveraging the quantified relationship between base and novel classes.
• To enhance the adaptation of base knowledge for novel instance learning.

Main Methods:

• EME maximizes base-class margins during initial training to create space for novel class adaptation.
• It quantifies interclass semantic relationships using equilibrium coefficients derived from base-class prototypes.
• Margin loss is reweighted using these coefficients, combined with instance disturbance (ID) augmentation.

Main Results:

• EME demonstrates consistent performance gains across various baseline methods and benchmarks.
• The method effectively adapts base knowledge for precise novel instance representation.
• EME proves to be a versatile, plug-and-play module applicable to few-shot classification as well.

Conclusions:

• Explicit Margin Equilibrium (EME) offers an effective solution for optimizing class margins in few-shot learning.
• The proposed scheme successfully balances the need for class discrimination and accurate representation.
• EME shows significant potential for improving few-shot object detection and classification tasks.