Libra R-CNN: Towards Balanced Learning for Object Detection
摘要
与模型体系结构相比，训练过程对于检测器的成功也是至关重要的，在对象检测中受到的关注相对较少。在这项工作中，我们仔细地重新审视了探测器的标准训练实践，发现探测性能通常受到训练过程中不平衡的限制，这通常包括三个层次 - 样本水平，特征水平和目标水平。为了减轻由此引起的不利影响，我们提出了Libra R-CNN，这是一个简单但有效的框架，用于物体检测的均衡学习。它集成了三个新颖的组件：IoU平衡采样，平衡特征金字塔和平衡L1损耗，分别用于减少样本，特征和客观水平的不平衡。得益于整体平衡设计，Libra R-CNN显着提高了检测性能。没有花里胡哨，它在MSCOCO上分别比FPN更快的R-CNN和RetinaNet高出2.5分和2.0分的平均精度（AP）。
Abstract
Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. 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 but effective framework towards balanced learning for object detection. It integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level. Benefitted from the overall balanced design, Libra R-CNN significantly improves the detection performance. Without bells and whistles, it achieves 2.5 points and 2.0 points higher Average Precision (AP) than FPN Faster R-CNN and RetinaNet respectively on MSCOCO.
| 标题 | 内容 | 时间 |
|---|---|---|
| Libra R-CNN: Towards Balanced Learning for Object Detection | 原始论文 | 20190404 |
| mmdetection | 论文实现 | |
| CVPR 2019 天秤座R-CNN：全面平衡的目标检测器 |
