Pattern Recognition
Pattern Recognition
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DOI：
10.2307/2982255
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通讯作者：
Yi Rong;Shengwu Xiong;Yongsheng Gao
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作者：
Yi Rong;Shengwu Xiong;Yongsheng Gao
现有的长尾识别方法侧重于通过重新加权、重新采样或全局表征学习来学习全局图像表征。然而，我们观察到，解决现实世界中的长尾识别问题需要对图像内的局部部分有精细的理解，以避免具有相似全局配置的图像之间的混淆。我们提出了一种基于局部伪属性（LPA）的新型自监督学习框架，该框架通过对局部特征进行聚类来学习，无需任何人工标注。与图像级别的类别标签相比，这种伪属性通常更加平衡。我们的方法在各种长尾图像分类数据集上，如CIFAR100 - LT、iNaturalist和ImageNet - LT，性能优于现有技术。
Existing long-tailed recognition methods focus on learning global image representation by re-weighing, re-sampling, or global representation learning. However, we observe that solving real-world long-tailed recognition problems requires a fine-grained understanding of local parts within the image in order to avoid confusion among images with similar global configurations. We propose a novel self-supervised learning framework based on local pseudo-attributes (LPA) that are learned via clustering of local features without any human annotations. Such pseudo-attributes are often more balanced compared to image-level class labels. Our method outperforms the state-of-the-art on various long-tailed image classification datasets, such as CIFAR100-LT, iNaturalist, and ImageNet-LT.