Fully Convolutional Instance-aware Semantic Segmentation

CVPR 2017  ·  Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei ·

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 proposed network is highly integrated and 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 \url{https://github.com/daijifeng001/TA-FCN}.

PDF Abstract CVPR 2017 PDF CVPR 2017 Abstract

Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Instance Segmentation COCO test-dev FCIS +OHEM mask AP 29.2% # 103
AP50 49.5% # 39
APS 7.1% # 38
APM 31.3% # 35
APL 50.0% # 32
Instance Segmentation COCO test-dev FCIS+++ +OHEM mask AP 33.6% # 98
AP50 54.5% # 36

Methods


No methods listed for this paper. Add relevant methods here