Region average pooling for context-aware object detection

Region average pooling for context-aware object detection

Object detection has been a key task in computer vision with deep convolutional neural networks being a significant performer. We propose a method named Region Average Pooling that leverages object co-occurrence to improve object detection performance. Given regions of interest in an image, our meth...

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Bibliographic Details
Main Authors: KUAN, Kingsley, MANEK, Gaurav, LIN, Jie, FANG, Yuan, CHANDRASEKHAR, Vijay
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Pooling
CNN
Faster R-CNN
Context
Object Detection
Object Co-occurrence
Databases and Information Systems
Graphics and Human Computer Interfaces
Online Access:https://ink.library.smu.edu.sg/sis_research/4072
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Institution: Singapore Management University
Language: English
Description
Summary:Object detection has been a key task in computer vision with deep convolutional neural networks being a significant performer. We propose a method named Region Average Pooling that leverages object co-occurrence to improve object detection performance. Given regions of interest in an image, our method augments object detection networks with pooled contextual features from other regions of interest in the scene. We implement our scheme and evaluate it on the Pascal Visual Object Classes (VOC) 2007 and Microsoft Common Objects in Context (MS COCO) datasets. When used as part of the Faster R-CNN object detection framework with VGG-16, we show an increase in mAP from 24.2% to 25.5% over baseline Faster R-CNN and Global Average Pooling when testing on MS COCO.

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