Multi-scale Location-Aware Kernel Representation for Object Detection

Hao Wang, Qilong Wang, Mingqi Gao, Peihua Li, Wangmeng Zuo
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Although Faster R-CNN and its variants have shown promising performance in object detection, they only exploit simple first-order representation of object proposals for final classification and regression. Recent classification methods demonstrate that the integration of highorder statistics into deep convolutional neural networks can achieve impressive improvement, but their goal is to model whole images by discarding location information so that they cannot be directly adopted to object
more » ... ion. In this paper, we make an attempt to exploit high-order statistics in object detection, aiming at generating more discriminative representations for proposals to enhance the performance of detectors. To this end, we propose a novel Multi-scale Location-aware Kernel Representation (MLKP) to capture high-order statistics of deep features in proposals. Our M-LKP can be efficiently computed on a modified multi-scale feature map using a low-dimensional polynomial kernel approximation. Moreover, different from existing orderless global representations based on high-order statistics, our proposed MLKP is location retentive and sensitive so that it can be flexibly adopted to object detection. Through integrating into Faster R-CNN schema, the proposed MLKP achieves very competitive performance with state-of-the-art methods, and improves Faster R-CNN by 4.9% (mAP), 4.7% (mAP) and 5.0% (AP at IOU=[0.5:0.05:0.95]) on PASCAL VOC 2007, VOC 2012 and MS COCO benchmarks, respectively. (a) Faster R-CNN [33] (b) HyperNet [24] (c) RON [23] (d) Our MLKP
doi:10.1109/cvpr.2018.00136 dblp:conf/cvpr/WangWGLZ18 fatcat:4s7mbdxpezcadn5kih4v2vsl7u