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Long-tailed data distributions pose challenges for a variety of domains likee-commerce, finance, biomedical science, and cyber security, where theperformance of machine learning models is often dominated by head categorieswhile tail categories are inadequately learned. This work aims to provide asystematic view of long-tailed learning with regard to three pivotal angles:(A1) the characterization of data long-tailedness, (A2) the data complexity ofvarious domains, and (A3) the heterogeneity of emerging tasks. We developHeroLT, a comprehensive long-tailed learning benchmark integrating 18state-of-the-art algorithms, 10 evaluation metrics, and 17 real-world datasetsacross 6 tasks and 4 data modalities. HeroLT with novel angles and extensiveexperiments (315 in total) enables effective and fair evaluation of newlyproposed methods compared with existing baselines on varying dataset types.Finally, we conclude by highlighting the significant applications oflong-tailed learning and identifying several promising future directions. Foraccessibility and reproducibility, we open-source our benchmark HeroLT andcorresponding results at https://github.com/SSSKJ/HeroLT.

Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox