A Late Fusion CNN for Digital Matting
This paper studies the structure of a deep convolutional neural network to predict the foreground alpha matte by taking a single RGB image as input. Our network is fully convolutional with two decoder branches for the foreground and background classification respectively. Then a fusion branch is used to integrate the two classification results which gives rise to alpha values as the soft segmentation result. This design provides more degrees of freedom than a single decoder branch for the network to obtain better alpha values during training. The network can implicitly produce trimaps without user interaction, which is easy to use for novices without expertise in digital matting. Experimental results demonstrate that our network can achieve high-quality alpha mattes for various types of objects and outperform the state-of-the-art CNN-based image matting methods on the human image matting task.
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MS COCO
Composition-1K
AIM-500
P3M-10k
AM-2k
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Image Matting | AIM-500 | LF | SAD | 191.74 | # 6 | |
| MSE | 0.0667 | # 5 | ||||
| MAD | 0.1130 | # 6 | ||||
| Conn. | 181.26 | # 6 | ||||
| Grad. | 63.51 | # 5 | ||||
| Image Matting | AM-2K | LF | SAD | 36.12 | # 8 | |
| MSE | 0.0116 | # 8 | ||||
| MAD | 0.0210 | # 8 | ||||
| Image Matting | P3M-10k | LF | SAD | 42.95 | # 7 | |
| MSE | 0.0191 | # 7 | ||||
| MAD | 0.0250 | # 7 |
