CoupleNet: Coupling Global Structure with Local Parts for Object Detection
Zhu, Yousong1,2; Zhao, Chaoyang1,2; Wang, Jinqiao1,2; Zhao, Xu1,2; Wu, Yi3; Lu, Hanqing1,2
2017
会议名称International Conference on Computer Vision
会议日期2017.10.22-10.29
会议地点Venice,Italy
摘要

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher
detection speed while keeping the detection performance, the global structure information is ignored by the positionsensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the coupling module which
consists of two branches. One branch adopts the positionsensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments
demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e.
a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4%
on COCO. Codes will be made publicly available.

文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20116
专题紫东太初大模型研究中心_图像与视频分析
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Nanjing Audit University
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Zhu, Yousong,Zhao, Chaoyang,Wang, Jinqiao,et al. CoupleNet: Coupling Global Structure with Local Parts for Object Detection[C],2017.
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