Long Tailed Image Recognition

Long-tailed image recognition addresses the challenge of training accurate image classifiers on datasets where some classes have far more examples than others. Current research focuses on improving model robustness to this imbalance through techniques like data augmentation (including synthetic data generation), loss function modifications (e.g., adjusting logits or re-weighting gradients), and incorporating visual-linguistic information. These advancements aim to enhance the representation of under-represented classes, leading to more equitable and accurate classification across all categories, with significant implications for real-world applications like object detection and image retrieval.