1 INTRODUCTION

1.1 Main Contributions of the Survey

2 BACKGROUND

2.1 Problem Definition

2.2 Performance Metrics

2.3 Datasets

3 TAXONOMY

3.1 Loss Re-weighting

3.2 Margin-Based Logit Adjustment

3.3 Optimized Representation Learning

3.4 Balanced Classifier Learning

4 LOSS RE-WEIGHTING

5 MARGIN-BASED LOGIT ADJUSTMENT

6 OPTIMIZED REPRESENTATION LEARNING

7 BALANCED CLASSIFIER LEARNING

8 RESEARCH GAPS

8.1 Zero-Shot Learning in the Context of Long-Tail Classification

8.2 Long-Tail Classification with Unknown or Dynamic Class Imbalance Ratios Under Online Settings

8.3 Classification Under Extreme Class Imbalance

9 CONCLUSION