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Local Pseudo-Attributes for Long-Tailed Recognition
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Dong-Jin Kim and Tsung-Wei Ke and Stella X. Yu
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Pattern Recognition Letters, 172:51-7, 2023
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Paper
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Journal
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Abstract
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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.
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Keywords
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long-tailed recognition, pseudo-attributes, self-supervised learning
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