Abstract
This paper considers deep visual recognition on long-tailed data, with the majority categories only occupying relatively few samples. The tail categories are prone to lack of within-class diversity, which compromises the representative ability of the learned visual concepts. A radical solution is to augment the tail categories with higher diversity. To this end, we introduce a simple and reliable method named Memory-based Jitter (MBJ) to gain extra diversity for the tail data. We observe that the deep model keeps on jittering from one historical edition to another, even when it already approaches convergence. The ``jitter'' means the small variations between historical models. We argue that such jitter largely originates from the within-class diversity of the overall data and thus encodes the within-class distribution pattern. To utilize such jitter for tail data augmentation, we store the jitter among historical models into a memory bank and get the so-called Memory-based Jitter. With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, \emph{i.e.}, image classification and deep metric learning (on long-tailed data). On image classification, MBJ collects the historical embeddings to learn an accurate classifier. In contrast, on deep metric learning, it collects the historical prototypes of each class to learn a robust deep embedding. Under both scenarios, MBJ enforces higher concentration on tail classes, so as to compensate for their lack of diversity. Extensive experiments on three long-tailed classification benchmarks and two deep metric learning benchmarks (person re-identification, in particular) demonstrate the significant improvement. Moreover, the achieved performance are on par with the state-of-the-art on both tasks.
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URL
https://arxiv.org/abs/2008.09809
