Foreground Feature Enhancement for Object Detection

Deep convolutional neural networks have shown great success in object detection. Most object detection methods focus on improving network architecture and introducing additional objective functions to improve the discrimination of object detectors, while the informative annotations of the training data obtained from enormous human effort are mainly used in the last stage of the network for producing supervisions, thus being under-explored. In this paper, we propose to take further advantage of bounding box annotations to highlight the feature map of foreground objects by erasing background noise with a novel Mask loss, in which process L-{2} norm is further incorporated to avoid degenerated features. The extensive experiments on PASCAL VOC 2007, VOC 2012, and COCO 2017 will demonstrate the proposed method can greatly improve detection performance compared with baseline models, thus achieving competitive results.