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Title: Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation
Authors: Zhang, Chi
Lin, Guosheng
Liu, Fayao
Guo, Jiushuang
Wu, Qingyao
Yao, Rui
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., & Yao, R. (2019). Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV). doi:10.1109/ICCV.2019.00968
Project: AISG-RP-2018-003 
RG126/17 (S) 
Abstract: One-shot image segmentation aims to undertake the segmentation task of a novel class with only one training image available. The difficulty lies in that image segmentation has structured data representations, which yields a many-to-many message passing problem. Previous methods often simplify it to a one-to-many problem by squeezing support data to a global descriptor. However, a mixed global representation drops the data structure and information of individual elements. In this paper, we propose to model structured segmentation data with graphs and apply attentive graph reasoning to propagate label information from support data to query data. The graph attention mechanism could establish the element-to-element correspondence across structured data by learning attention weights between connected graph nodes. To capture correspondence at different semantic levels, we further propose a pyramid-like structure that models different sizes of image regions as graph nodes and undertakes graph reasoning at different levels. Experiments on PASCAL VOC 2012 dataset demonstrate that our proposed network significantly outperforms the baseline method and leads to new state-of-the-art performance on 1-shot and 5-shot segmentation benchmarks.
DOI: 10.1109/ICCV.2019.00968
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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