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Fully Convolutional Instance-aware Semantic Segmentation

About

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}.

Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei• 2016

Related benchmarks

TaskDatasetResultRank
Instance SegmentationCOCO (test-dev)
APM31.3
380
Instance SegmentationCOCO 2017 (test-dev)
AP (Overall)33.6
253
Instance SegmentationMS COCO (test-dev)
mAP@[.5:.95]37.6
46
Instance SegmentationSBD (val)
AP@0.50 (Mask)65.7
22
Instance SegmentationPascal SBD 2012--
17
Amodal Instance SegmentationKINS (test)
Amodal AP23.5
16
Object DetectionKINS (test)
APdet25.6
9
Instance SegmentationCOCO Person category (test-dev)
AP38.6
6
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Other info

Code

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