期刊
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
卷 -, 期 -, 页码 7535-7544出版社
IEEE
DOI: 10.1109/ICCV48922.2021.00746
关键词
-
资金
- National Natural Science Foundation of China [61972067]
- Innovation Technology Funding of Dalian [2020JJ26GX036]
- PicUP.Ai project of the Winroad Holdings Ltd.
With the development of deep convolutional neural networks, the Tripartite Information Mining and Integration Network (TIMI-Net) is proposed to harmonize the coordination between global and local attributes, achieving effective interaction through a three-branch encoder and TI2 Module. The research also introduces a large-scale human matting dataset (Human-2K) and conducts extensive experiments to demonstrate the superior performance of TIMI-Net on various datasets.
With the development of deep convolutional neural networks, image matting has ushered in a new phase. Regarding the nature of image matting, most researches have focused on solutions for transition regions. However, we argue that many existing approaches are excessively focused on transition-dominant local fields and ignored the inherent coordination between global information and transition optimisation. In this paper, we propose the Tripartite Information Mining and Integration Network (TIMI-Net) to harmonize the coordination between global and local attributes formally. Specifically, we resort to a novel 3-branch encoder to accomplish comprehensive mining of the input information, which can supplement the neglected coordination between global and local fields. In order to achieve effective and complete interaction between such multi-branches information, we develop the Tripartite Information Integration (TI2) Module to transform and integrate the interconnections between the different branches. In addition, we built a large-scale human matting dataset (Human-2K) to advance human image matting, which consists of 2100 high-precision human images (2000 images for training and 100 images for test). Finally, we conduct extensive experiments to prove the performance of our proposed TIMI-Net, which demonstrates that our method performs favourably against the SOTA approaches on the alphamatting.com (Rank First), Composition-1K (MSE-0.006, Grad-11.5), Distinctions-646 and our Human-2K. Also, we have developed an online evaluation website to perform natural image matting.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据