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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Instance Segmentation | COCO (test-dev) | APM31.3 | 380 | |
| Instance Segmentation | COCO 2017 (test-dev) | AP (Overall)33.6 | 253 | |
| Instance Segmentation | MS COCO (test-dev) | mAP@[.5:.95]37.6 | 46 | |
| Instance Segmentation | SBD (val) | AP@0.50 (Mask)65.7 | 22 | |
| Instance Segmentation | Pascal SBD 2012 | -- | 17 | |
| Amodal Instance Segmentation | KINS (test) | Amodal AP23.5 | 16 | |
| Object Detection | KINS (test) | APdet25.6 | 9 | |
| Instance Segmentation | COCO Person category (test-dev) | AP38.6 | 6 |
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