We investigate the problem of reducing mistake severity for fine-grained classification. Fine-grained classification can be challenging, mainly due to the requirement of knowledge or domain expertise for accurate annotation. However, humans are particularly adept at performing coarse classification as it requires relatively low levels of expertise. To this end, we present a novel approach for Post-Hoc Correction called Hierarchical Ensembles (HiE) that utilizes label hierarchy to improve the performance of fine-grained classification at test-time using the coarse-grained predictions. By only requiring the parents of leaf nodes, our method significantly reduces avg. mistake severity while improving top-1 accuracy on the iNaturalist-19 and tieredImageNet-H datasets, achieving a new state-of-the-art on both benchmarks. We also investigate the efficacy of our approach in the semi-supervised setting. Our approach brings notable gains in top-1 accuracy while significantly decreasing the severity of mistakes as training data decreases for the fine-grained classes. The simplicity and post-hoc nature of HiE render it practical to be used with any off-the-shelf trained model to improve its predictions further.

翻译：我们研究了减少细粒度分类中错误严重性的问题。细粒度分类可能具有挑战性，主要是因为需要知识或领域专业知识才能进行准确标注。然而，人类特别擅长进行粗分类，因为这只需要相对较低的专业水平。为此，我们提出了一种名为层次集成（HiE）的事后修正新方法，该方法利用标签层次结构在测试时通过粗粒度预测来改进细粒度分类的性能。通过仅需要叶节点的父节点，我们的方法显著降低了平均错误严重性，同时在iNaturalist-19和tieredImageNet-H数据集上提高了top-1准确率，在两个基准上均达到了新的最先进水平。我们还研究了我们的方法在半监督设置中的有效性。随着细粒度类别训练数据的减少，我们的方法在top-1准确率上取得显著提升，同时大大降低了错误严重性。HiE的简单性和事后性使其能够与任何现成的训练模型一起使用，以进一步改进其预测。