论文标题
通过显着图的黑盒解释对象探测器
Black-box Explanation of Object Detectors via Saliency Maps
论文作者
论文摘要
我们提出了D-Rise，这是一种为对象探测器预测的视觉解释的方法。利用对象检测的本地化和分类方面的提议相似性度量标准，我们的方法可以产生显着图，以显示最大的图像区域，从而影响预测。在软件测试意义上，D-Rise可以视为“黑框”，因为它只需要访问对象检测器的输入和输出。与基于梯度的方法相比，D-Rise对正在测试的特定类型的对象检测器的类型更一般和不可知，并且不需要了解模型的内部工作。我们表明，D-Rise可以轻松地应用于不同的对象检测器，包括一阶段检测器，例如Yolov3和两个阶段检测器，例如更快的RCNN。我们对生成的视觉解释进行了详细的分析，以突出对象检测器学到的上下文的利用和可能的偏差。
We propose D-RISE, a method for generating visual explanations for the predictions of object detectors. Utilizing the proposed similarity metric that accounts for both localization and categorization aspects of object detection allows our method to produce saliency maps that show image areas that most affect the prediction. D-RISE can be considered "black-box" in the software testing sense, as it only needs access to the inputs and outputs of an object detector. Compared to gradient-based methods, D-RISE is more general and agnostic to the particular type of object detector being tested, and does not need knowledge of the inner workings of the model. We show that D-RISE can be easily applied to different object detectors including one-stage detectors such as YOLOv3 and two-stage detectors such as Faster-RCNN. We present a detailed analysis of the generated visual explanations to highlight the utilization of context and possible biases learned by object detectors.
