Fully Convolutional Instance-Aware Semantic Segmentation

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 [29] and instance mask proposal [5]. It performs instance mask prediction and classification jointly. The underlying convolutional...

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Bibliographic Details
Published in:2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 4438 - 4446
Main Authors: Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2017
Subjects:
Convolution
Convolutional codes
Image segmentation
Object segmentation
Proposals
Semantics
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary: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 [29] and instance mask proposal [5]. 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 at https://github.com/daijifeng001/TA-FCN.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2017.472