Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Title | Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation |
Publication Type | Unpublished |
Year of Publication | 2014 |
Authors | Girshick, R., Donahue J., Darrell T., & Malik J. |
Other Numbers | 3687 |
Abstract | 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 data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also compare R-CNN to OverFeat, a recently proposed sliding-window detector based on a similar CNN architecture. We find that R-CNN outperforms OverFeat by a large margin on the 200-class ILSVRC2013 detection dataset. Source code for the complete system is available at this http URL |
Bibliographic Notes | Technical Report, Preprint: arXiv:1311.2524 |
Abbreviated Authors | R. Girshick, J. Donahue, T. Darrell, and J. Malik |
ICSI Research Group | Vision |
ICSI Publication Type | Unpublished |