Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120111
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Type: Conference paper
Title: RefineNet: multi-path refinement networks with identity mappings for high-resolution semantic segmentation
Author: Lin, G.
Milan, A.
Shen, C.
Reid, I.
Citation: Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition, 2017 / vol.2017-January, pp.5168-5177
Publisher: IEEE
Publisher Place: online
Issue Date: 2017
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781538604571
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (21 Jul 2017 - 26 Jul 2017 : Honolulu, Hawaii)
Statement of
Responsibility: 
Guosheng Lin, Anton Milan, Chunhua Shen, Ian Reid
Abstract: Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.
Rights: © 2017 IEEE
RMID: 0030084927
DOI: 10.1109/CVPR.2017.549
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/FT120100969
http://purl.org/au-research/grants/arc/FL130100102
Appears in Collections:Computer Science publications

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