论文标题
评估深层对象探测器的上下文
Evaluating Context for Deep Object Detectors
论文作者
论文摘要
哪个对象检测器适合您的上下文敏感任务？深对象检测器利用场景上下文以不同的方式识别。在本文中，我们将对象探测器从上下文中分为3个类别：通过裁剪输入（RCNN），部分上下文，通过裁剪特征图（两阶段方法）（两阶段方法）和完整的上下文，而无需任何裁剪（单级方法）。我们系统地评估了每个深度检测器类别的上下文效果。我们为不同上下文创建一个完全控制的数据集并研究深探测器的上下文。我们还逐渐评估了MS Coco上的背景上下文和前景对象。我们证明，单阶段和两阶段对象检测器可以并且将通过其大型接受场来使用上下文。因此，选择最佳对象检测器可能取决于应用程序上下文。
Which object detector is suitable for your context sensitive task? Deep object detectors exploit scene context for recognition differently. In this paper, we group object detectors into 3 categories in terms of context use: no context by cropping the input (RCNN), partial context by cropping the featuremap (two-stage methods) and full context without any cropping (single-stage methods). We systematically evaluate the effect of context for each deep detector category. We create a fully controlled dataset for varying context and investigate the context for deep detectors. We also evaluate gradually removing the background context and the foreground object on MS COCO. We demonstrate that single-stage and two-stage object detectors can and will use the context by virtue of their large receptive field. Thus, choosing the best object detector may depend on the application context.
