State-of-the-art object detection approaches are often composed of two stages, namely, proposing a number of regions on an image and classifying each of them into one class. Both stages share a network backbone which builds visual features in a bottom-up manner. In this paper, we advocate the importance of equipping two-stage detectors with top-down signals, in order to which provides high-level contextual cues to complement low-level features. In practice, this is implemented by adding a side path in the detection head to predict all object classes in the image, which is co-supervised by image-level semantics and requires little extra overheads. Our approach is easily applied to two popular object detection algorithms, and achieves consistent performance gain in the MS-COCO dataset.

最先进的目标检测方法通常由两个阶段组成，即在图像上提出多个区域并将它们中的每一个分类为一个类别。这两个阶段共享一个网络骨干，以自下而上的方式构建视觉特征。在本文中，我们提倡为两级检测器配备自上而下的信号的重要性，以便提供高级上下文线索来补充低级特征。在实践中，这是通过在检测头中添加一条侧路径来预测图像中的所有对象类别来实现的，这是由图像级语义共同监督的，几乎不需要额外的开销。我们的方法很容易应用于两种流行的对象检测算法，并在 MS-COCO 数据集中实现一致的性能提升。