Lightweight high-resolution Subject Matting in the Real World
Peng Liu Fanyi Wang Jingwen Su Yanhao Zhang Guojun Qi
Existing saliency object detection (SOD) methods struggle to satisfy fast
inference and accurate results simultaneously in high resolution scenes. They
are limited by the quality of public datasets and efficient network modules for
high-resolution images. To alleviate these issues, we propose to construct a
saliency object matting dataset HRSOM and a lightweight network PSUNet.
Considering efficient inference of mobile depolyment framework, we design a
symmetric pixel shuffle module and a lightweight module TRSU. Compared to 13
SOD methods, the proposed PSUNet has the best objective performance on the
high-resolution benchmark dataset. Evaluation results of objective assessment
are superior compared to U$^2$Net that has 10 times of parameter amount of our
network. On Snapdragon 8 Gen 2 Mobile Platform, inference a single
640$\times$640 image only takes 113ms. And on the subjective assessment,
evaluation results are better than the industry benchmark IOS16 (Lift subject
from background).