Long Tailed Recognition

Long-tailed recognition addresses the challenge of training machine learning models on datasets where class frequencies are highly skewed, leading to poor performance on under-represented classes. Current research focuses on improving model robustness through techniques like data augmentation (e.g., CutMix, generative models), loss function modifications (e.g., contrastive learning, re-weighting), and novel architectures (e.g., dual-branch networks, multi-expert models) to better handle class imbalance. This field is crucial for improving the reliability of AI systems in real-world applications where data naturally exhibits long-tailed distributions, impacting areas such as image classification, speech recognition, and semantic segmentation.