Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition. (arXiv:2207.12808v3 [cs.CV] UPDATED)

Data augmentation for minority classes is an effective strategy for
long-tailed recognition, thus developing a large number of methods. Although
these methods all ensure the balance in sample quantity, the quality of the
augmented samples is not always satisfactory for recognition, being prone to
such problems as over-fitting, lack of diversity, semantic drift, etc. For
these issues, we propose the Class-aware Universum Inspired Re-balance
Learning(CaUIRL) for long-tailed recognition, which endows the Universum with
class-aware ability to re-balance individual minority classes …

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