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Benchmark datasets for visual recognition assume that data is uniformlydistributed, while real-world datasets obey long-tailed distribution. Currentapproaches handle the long-tailed problem to transform the long-tailed datasetto uniform distribution by re-sampling or re-weighting strategies. Theseapproaches emphasize the tail classes but ignore the hard examples in headclasses, which result in performance degradation. In this paper, we propose anovel gradient harmonized mechanism with category-wise adaptive precision todecouple the difficulty and sample size imbalance in the long-tailed problem,which are correspondingly solved via intra- and inter-category balancestrategies. Specifically, intra-category balance focuses on the hard examplesin each category to optimize the decision boundary, while inter-categorybalance aims to correct the shift of decision boundary by taking each categoryas a unit. Extensive experiments demonstrate that the proposed methodconsistently outperforms other approaches on all the datasets.

Solving The Long-Tailed Problem via Intra- and Inter-Category Balance