Papers › Fully Convolutional Instance-aware Semantic Segmentation
Fully Convolutional Instance-aware Semantic Segmentation
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}.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | COCO test-dev | FCIS+++ +OHEM | AP50 | 54.5% | #100 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | FCIS+++ +OHEM | mask AP | 33.6% | #100 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | FCIS +OHEM | AP50 | 49.5% | #105 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | FCIS +OHEM | APL | 50.0% | #105 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | FCIS +OHEM | APM | 31.3% | #105 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | FCIS +OHEM | APS | 7.1% | #105 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | FCIS +OHEM | mask AP | 29.2% | #105 of 112 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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