Instance recognition is rapidly advanced along with the developments of various deep convolutional neural networks. Compared to the architectures of networks, the training process, which is also crucial to the success of detectors, has received relatively less attention. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level. To mitigate the adverse effects caused thereby, we propose Libra R-CNN, a simple yet effective framework towards balanced learning for instance recognition. It integrates IoU-balanced sampling, balanced feature pyramid, and objective re-weighting, respectively for reducing the imbalance at sample, feature, and objective level. Extensive experiments conducted on MS COCO, LVIS and Pascal VOC datasets prove the effectiveness of the overall balanced design.

本文提出了一种针对实例识别的平衡学习框架Libra R-CNN，其中包括IoU平衡采样、平衡特征金字塔和目标重加权，以分别减少样本级别、特征级别和目标级别的不平衡性，通过对MS COCO、LVIS和Pascal VOC等数据集的广泛实验，证明了整体平衡设计的有效性。

实例识别的平衡学习方法研究