%0 Conference Paper %B Conference on Computer Vision and Pattern Recognition (CVPR) %D 2018 %T Single-Shot Object Detection with Enriched Semantics %A Zhishuai Zhang %A Siyuan Qiao %A Cihang Xie %A Wei Shen %A Bo Wang %A Alan Yuille %X

We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a global activation module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.7 on VOC2007 test and an mAP of 32.8 on COCO test-dev with an inference speed of 31.5 milliseconds per image on a Titan Xp GPU. With a lower resolution version, we achieve an mAP of 79.7 on VOC2007 with an inference speed of 13.0 milliseconds per image.

%B Conference on Computer Vision and Pattern Recognition (CVPR) %C Salt Lake City, Utah %8 06/2018 %G eng %U http://cvpr2018.thecvf.com/