title: Region-Based Convolutional Networks for Accurate Object Detection and Segmentation.
abstract: Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined 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 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.
authors.forename: Ross
authors.initials: R
authors.lastname: Girshick
authors.forename: Jeff
authors.initials: J
authors.lastname: Donahue
authors.forename: Trevor
authors.initials: T
authors.lastname: Darrell
authors.forename: Jitendra
authors.initials: J
authors.lastname: Malik