Key Point Sensitive Loss for Long-Tailed Visual Recognition.

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Existing classification models struggle with long-tailed data distributions, overemphasizing head classes and neglecting tail classes.
• This imbalance leads to poor generalization capability and biased predictions, particularly affecting minority classes.

Purpose of the Study:

• To develop a novel approach that enhances classification model performance on long-tailed datasets.
• To improve generalization capability by effectively addressing the underrepresentation of tail classes.

Main Methods:

• Introduction of a key point sensitive (KPS) loss function to strongly regularize key points and improve generalization.
• Implementation of a gradient adjustment (GA) optimization strategy to re-balance gradients for positive and negative samples per class.
• KPS loss assigns larger margins to tail classes to boost their performance.

Main Results:

• The proposed KPS loss and GA strategy significantly improve classification accuracy for tail classes.
• The method maintains competent performance on head classes, demonstrating a balanced improvement.
• Gradient analysis revealed that tail classes receive negative signals, which the GA strategy effectively mitigates.

Conclusions:

• The novel KPS loss and GA optimization strategy effectively address the challenges of long-tailed data distributions in classification.
• The proposed method offers a robust solution for improving model generalization and accuracy on imbalanced datasets.