Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resampling is typically introduced as a simple but effective approach. However, we observe that long-tailed detection differs from classification since multiple classes may be present in one image. As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level. We address object-level resampling by introducing an object-centric memory replay strategy based on dynamic, episodic memory banks. Our proposed strategy has two benefits: 1) convenient object-level resampling without significant extra computation, and 2) implicit feature-level augmentation from model updates. We show that image-level and object-level resamplings are both important, and thus unify them with a joint resampling strategy (RIO). Our method outperforms state-of-the-art long-tailed detection and segmentation methods on LVIS v0.5 across various backbones.

该研究针对长尾分布数据集的检测问题，引入了基于对象中心的记忆重放策略，采用动态的场景式记忆库来实现对象级别重新采样，进而实现图像级别和对象级别的联合采样策略，提出的方法在 LVIS v0.5 上超过当前长尾检测和分割方法的状态。

图像级或对象级？两种重采样策略在长尾检测中的比较