Abstract
Previous natural image matting algorithms have difficulties with transition regions in the foreground and background, such as tiny and detailed structures like hair. This paper argues that more efficient low-level features can help the network recover details with minor increases in network capacity and computational complexity. The proposed method, termed low-level feature channel guidance net LFCGN, has two advantages: 1) it introduces a low-level feature channel attention module designed to make the model parameters more efficient and can even lead to high-level feature map generation. 2) a dynamic upsampling is used in the decoder stage, making the detail part recover more efficiently. Experiments are evaluated on the Composition-1k dataset, and the experimental results show that our method obtained competitive performance compared to the state-of-the-art on task of image matting.
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Jiang, H., Wu, S., He, D., Xiao, G. (2022). Natural Image Matting with Low-Level Feature Attention Guidance. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_44
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