publication . Other literature type . Preprint . Conference object . 2013

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Ross Girshick; Jeff Donahue; Trevor Darrell; Jitendra Malik;
  • Published: 11 Nov 2013
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012---achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training ...
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Convolutional neural network, Source code, media_common.quotation_subject, media_common, Object detection, Ensemble systems, Pattern recognition, Feature (computer vision), Computer vision, Segmentation, Artificial intelligence, business.industry, business, Hierarchy, Machine learning, computer.software_genre, computer, Scalability, Computer science
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