Solving The Long-Tailed Problem via Intra- and Inter-Category Balance
- URL: http://arxiv.org/abs/2204.09234v2
- Date: Fri, 22 Apr 2022 05:59:47 GMT
- Title: Solving The Long-Tailed Problem via Intra- and Inter-Category Balance
- Authors: Renhui Zhang, Tiancheng Lin, Rui Zhang, Yi Xu
- Abstract summary: Benchmark datasets for visual recognition assume that data is uniformly distributed, while real-world datasets obey long-tailed distribution.
Current approaches handle the long-tailed problem to transform the long-tailed dataset to uniform distribution by re-sampling or re-weighting strategies.
We propose a novel gradient harmonized mechanism with category-wise adaptive precision to decouple the difficulty and sample size imbalance in the long-tailed problem.
- Score: 17.04366558952357
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Benchmark datasets for visual recognition assume that data is uniformly
distributed, while real-world datasets obey long-tailed distribution. Current
approaches handle the long-tailed problem to transform the long-tailed dataset
to uniform distribution by re-sampling or re-weighting strategies. These
approaches emphasize the tail classes but ignore the hard examples in head
classes, which result in performance degradation. In this paper, we propose a
novel gradient harmonized mechanism with category-wise adaptive precision to
decouple the difficulty and sample size imbalance in the long-tailed problem,
which are correspondingly solved via intra- and inter-category balance
strategies. Specifically, intra-category balance focuses on the hard examples
in each category to optimize the decision boundary, while inter-category
balance aims to correct the shift of decision boundary by taking each category
as a unit. Extensive experiments demonstrate that the proposed method
consistently outperforms other approaches on all the datasets.
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