What problems are there in detecting long-tail classes?

Problems in detecting long-tail classes are related to the fact that such classes occur very rarely in the data, which leads to several difficulties:

• Lack of training data: Few examples for rare classes make it difficult for the model to learn, it generalizes poorly and often misses these objects.
• Class imbalance: The model tends to focus on frequent classes, ignoring rare ones, which reduces accuracy on long-tail classes.
• Overfitting issues: When trying to improve detection of rare classes, the model may overfit on a limited set of examples.
• Annotation difficulties: Rare objects are harder to find and annotate, increasing the cost and time for data preparation.

To address these problems, techniques such as:

• Data augmentation for rare classes.
• Using balancing methods, such as loss weighting.
• Transfer learning and few-shot learning.
• Specialized architectures and loss functions that account for imbalance.