3 papers - avg viability 7.0
Leveraging large language models to guide incremental learning for imbalanced datasets, improving performance on rare classes.
A framework that enhances incremental learning using vision-language models with adaptive connections for improved efficiency.
One-A is a unified framework for class-incremental learning that adapts to imbalanced task streams while maintaining efficiency.