REGION AVERAGE POOLING FOR CONTEXT-AWARE OBJECT DETECTION

REGION AVERAGE POOLING FOR CONTEXT-AWARE OBJECT DETECTION
Title:
REGION AVERAGE POOLING FOR CONTEXT-AWARE OBJECT DETECTION
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2017 IEEE International Conference on Image Processing
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Publication Date:
17 September 2017
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Abstract:
Object detection has been a key task in computer vision with deep convolutional neural networks being a significant per- former. We propose a method named Region Average Pooling that leverages object co-occurrence to improve object detec- tion performance. Given regions of interest in an image, our method augments object detection networks with pooled con- textual features from other regions of interest in the scene. We implement our scheme and evaluate it against the Pascal Visual Object Classes (VOC) 2007 [1, 2] and Microsoft Com- mon Objects in Context (MS COCO) [3] datasets. When used as part of the Faster R-CNN object detection framework with VGG-16 [4], we show an increase in mAP (0.5:0.95) from 24.2% to 25.5% over baseline Faster R-CNN and Global Av- erage Pooling when testing on MS COCO.
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