ACVM: An Adaptive Combination Validation Mechanism for Long-Tailed Image Recognition

In real-world scenarios, large-scale datasets often exhibit a long-tailed data distribution. Training deep neural networks on such data typically leads to a bias towards head classes. Existing studies have demonstrated that the reweighting strategy is an effective means to alleviate the long-tailed issue. Recent studies suggest that incorporating class difficulty into reweighting can yield superior results. However, the method of quantifying class difficulty by an independent validation set has shown limitations in practical applications, i.e., wasting training samples and inaccurate estimations. To address this issue, this study proposes a novel model based on K-fold cross-validation, called the adaptive combination validation model, which contains two main innovations: first, both class and sample difficulty are quantified by using a more comprehensive and authentic estimation strategy, i.e., K-fold cross-validation, to obtain accurate and robust estimations; second, we extract the prediction probability distributions of samples, which reflect sample difficulty, from different model branches and design a distribution-harmonized loss to simultaneously focus on the effects of reweighted and original distributions. Extensive experiments on several popular long-tailed image recognition datasets (CIFAR10-LT and CIFAR100-LT, with several varying imbalance rates, and ImageNet-LT) demonstrate that the proposed method can effectively alleviate the long-tailed issue and achieve state-of-the-art performance on most datasets.