Browsing by Author "Yang, Xin"

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    GlassNet: Label Decoupling‐based Three‐stream Neural Network for Robust Image Glass Detection
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Zheng, Chengyu; Shi, Ding; Yan, Xuefeng; Liang, Dong; Wei, Mingqiang; Yang, Xin; Guo, Yanwen; Xie, Haoran; Hauser, Helwig and Alliez, Pierre
    Most of the existing object detection methods generate poor glass detection results, due to the fact that the transparent glass shares the same appearance with arbitrary objects behind it in an image. Different from traditional deep learning‐based wisdoms that simply use the object boundary as an auxiliary supervision, we exploit label decoupling to decompose the original labelled ground‐truth (GT) map into an interior‐diffusion map and a boundary‐diffusion map. The GT map in collaboration with the two newly generated maps breaks the imbalanced distribution of the object boundary, leading to improved glass detection quality. We have three key contributions to solve the transparent glass detection problem: (1) We propose a three‐stream neural network (call GlassNet for short) to fully absorb beneficial features in the three maps. (2) We design a multi‐scale interactive dilation module to explore a wider range of contextual information. (3) We develop an attention‐based boundary‐aware feature Mosaic module to integrate multi‐modal information. Extensive experiments on the benchmark dataset exhibit clear improvements of our method over SOTAs, in terms of both the overall glass detection accuracy and boundary clearness.
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    Multi-scale Information Assembly for Image Matting
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Qiao, Yu; Liu, Yuhao; Zhu, Qiang; Yang, Xin; Wang, Yuxin; Zhang, Qiang; Wei, Xiaopeng; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Image matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images.We argue that the foreground objects can be represented by different-level information, including the central bodies, large-grained boundaries, refined details, etc. Based on this observation, in this paper, we propose a multi-scale information assembly framework (MSIA-matte) to pull out high-quality alpha mattes from single RGB images. Technically speaking, given an input image, we extract advanced semantics as our subject content and retain initial CNN features to encode different-level foreground expression, then combine them by our well-designed information assembly strategy. Extensive experiments can prove the effectiveness of the proposed MSIA-matte, and we can achieve state-of-the-art performance compared to most existing matting networks.