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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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Neural Information Processing Systems Workshop on Self-Supervised Learning: Theory and Practice, New Orleans, Louisiana, 3 December 2022
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Paper
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Abstract
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We observe that solving the long-tailed distribution problems in real-world image datasets requires a fine-grained understanding of the local parts in an image. We propose a novel self-supervised learning framework with a new concept we call local pseudo-attribute (LPA) that is learned via clustering with local features without any extra human annotation. Note that the pseudo-attributes can be more balanced compared to the image-level class labels. Our final method using local pseudo-attributes achieves state-of-the-art performance through the experiments on various image classification setups with long-tailed distribution, such as CIFAR100- LT, iNaturalist, and ImageNet-LT datasets compared to the current long-tailed recognition methods.
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Keywords
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attributes, local pseudo-attributes, long-tailed recognition
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