  In the portrait matting, the goal is to predict an alpha matte that
identifies the effect of each pixel on the foreground subject. Traditional
approaches and most of the existing works utilized an additional input, e.g.,
trimap, background image, to predict alpha matte. However, (1) providing
additional input is not always practical, and (2) models are too sensitive to
these additional inputs. To address these points, in this paper, we introduce
an additional input-free approach to perform portrait matting. We divide the
task into two subtasks, segmentation and alpha matte prediction. We first
generate a coarse segmentation map from the input image and then predict the
alpha matte by utilizing the image and segmentation map. Besides, we present a
segmentation encoding block to downsample the coarse segmentation map and
provide useful feature representation to the residual block, since using a
single encoder causes the vanishing of the segmentation information. We tested
our model on four different benchmark datasets. The proposed method
outperformed the MODNet and MGMatting methods that also take a single input.
Besides, we obtained comparable results with BGM-V2 and FBA methods that
require additional input.
