Fully Convolutional Instance-Aware Semantic Segmentation is a research paper published in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). On theSindex it has a DataRank of 1.1. It has been cited 1,129 times.
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.
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Base Score Contribution
1.1
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0
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