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.
翻译:与网络结构相比,对探测器的成功至关重要的培训过程相对较少受到重视,在这项工作中,我们仔细地重新审视了探测器的标准培训做法,发现在培训过程中,检测性能往往受到不平衡的限制,这种不平衡一般分为三个层次:抽样水平、特征水平和客观水平;为了减轻由此造成的有害影响,我们提议Libra R-CNN,这是实现平衡学习的简单而有效的框架,例如承认;它结合了IoU平衡抽样、平衡地貌金字塔和客观的重新加权,分别用于减少抽样、特征和客观水平上的不平衡;对MS COCO、LVIS和Pascal VOC数据集进行了广泛的实验,证明了总体平衡设计的有效性。