Long Tail Recognition

Long-tail recognition addresses the challenge of machine learning models performing poorly on under-represented classes in datasets with skewed distributions. Current research focuses on improving model performance on these "tail" classes through techniques like generative models to synthesize additional data, hierarchical expert models to better handle diverse label distributions, and methods that leverage latent features or knowledge transfer from well-represented classes. These advancements are crucial for improving the robustness and generalizability of machine learning systems in real-world applications where data imbalances are common, impacting fields like image classification, video analysis, and recommendation systems.