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Training on datasets with long-tailed distributions has been challenging formajor recognition tasks such as classification and detection. To deal with thischallenge, image resampling is typically introduced as a simple but effectiveapproach. However, we observe that long-tailed detection differs fromclassification since multiple classes may be present in one image. As a result,image resampling alone is not enough to yield a sufficiently balanceddistribution at the object level. We address object-level resampling byintroducing an object-centric memory replay strategy based on dynamic, episodicmemory banks. Our proposed strategy has two benefits: 1) convenientobject-level resampling without significant extra computation, and 2) implicitfeature-level augmentation from model updates. We show that image-level andobject-level resamplings are both important, and thus unify them with a jointresampling strategy (RIO). Our method outperforms state-of-the-art long-taileddetection and segmentation methods on LVIS v0.5 across various backbones. Codeis available at https://github.com/NVlabs/RIO.

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection