Fully Convolutional Instance-Aware Semantic Segmentation

Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2359-2367

Abstract


We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation and instance mask proposal. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest. The network architecture is highly integrated and efficient. It achieves state-of-the-art performance in both accuracy and efficiency. It wins the COCO 2016 segmentation competition by a large margin. Code would be released.

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[bibtex]
@InProceedings{Li_2017_CVPR,
author = {Li, Yi and Qi, Haozhi and Dai, Jifeng and Ji, Xiangyang and Wei, Yichen},
title = {Fully Convolutional Instance-Aware Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}