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A Long-Tailed Image Classification Method Based on Enhanced Contrastive Visual Language.

Ying Song1,2, Mengxing Li1,2, Bo Wang3

  • 1Beijing Key Laboratory of Internet Culture and Digital Dissemination, Beijing Information Science and Technology University, Beijing 100101, China.

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Summary
This summary is machine-generated.

This study introduces a new long-tailed image classification method using enhanced contrastive visual language. It significantly improves accuracy for minority classes and reduces the performance gap between head and tail classes.

Keywords:
contrastive learningdata augmentationlong-tailed image classification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Long-tailed classification in machine learning presents challenges due to imbalanced datasets, where common methods neglect semantic label features, leading to significant accuracy disparities between majority (head) and minority (tail) classes.
  • Existing approaches often fail to leverage the rich semantic information present in original label text, exacerbating the accuracy gap between head and tail classes in image classification tasks.

Purpose of the Study:

  • To address the limitations of conventional long-tailed classification methods by incorporating semantic features from image label text.
  • To reduce the significant accuracy differences between majority and minority classes in long-tailed image classification.
  • To enhance the learning capabilities for tail class samples, thereby improving overall model performance on imbalanced datasets.

Main Methods:

  • A novel long-tailed image classification method based on enhanced contrastive visual language is proposed.
  • The method involves separate training for head and tail class samples and utilizes text-image pre-training.
  • Enhanced momentum contrastive loss and RandAugment are employed to improve the learning of tail class samples.

Main Results:

  • The proposed method demonstrated improvements across all metrics on the ImageNet-LT dataset, including a 3.4% increase in overall accuracy, 7.6% in tail class accuracy, and 3.5% in middle class accuracy.
  • The F1 score saw a substantial increase of 11.2% compared to the BALLAD method.
  • The accuracy difference between head and tail classes was reduced by 1.6% relative to the BALLAD method, indicating a more balanced performance.

Conclusions:

  • The enhanced contrastive visual language-based method effectively improves the performance of tail classes in long-tailed image classification.
  • This approach successfully reduces the accuracy disparity between majority and minority classes, offering a more robust solution for imbalanced datasets.
  • The findings suggest that integrating semantic label text features and advanced contrastive learning techniques is crucial for advancing long-tailed image classification.