  We propose the gradient-weighted Object Detector Activation Maps (ODAM), a
visualized explanation technique for interpreting the predictions of object
detectors. Utilizing the gradients of detector targets flowing into the
intermediate feature maps, ODAM produces heat maps that show the influence of
regions on the detector's decision for each predicted attribute. Compared to
previous works classification activation maps (CAM), ODAM generates
instance-specific explanations rather than class-specific ones. We show that
ODAM is applicable to both one-stage detectors and two-stage detectors with
different types of detector backbones and heads, and produces higher-quality
visual explanations than the state-of-the-art both effectively and efficiently.
We next propose a training scheme, Odam-Train, to improve the explanation
ability on object discrimination of the detector through encouraging
consistency between explanations for detections on the same object, and
distinct explanations for detections on different objects. Based on the heat
maps produced by ODAM with Odam-Train, we propose Odam-NMS, which considers the
information of the model's explanation for each prediction to distinguish the
duplicate detected objects. We present a detailed analysis of the visualized
explanations of detectors and carry out extensive experiments to validate the
effectiveness of the proposed ODAM.
