计算机科学
增采样
分割
图形
特征(语言学)
人工智能
特征学习
模式识别(心理学)
依赖关系图
理论计算机科学
图像(数学)
语言学
哲学
作者
Xue Xia,Jane You,Yuming Fang
标识
DOI:10.1007/978-981-99-8540-1_30
摘要
To extract cues for pixelwise segmentation in an efficient way, this paper proposes a lightweight model that involves graph structure in the convolutional network. First, a cross-layer module is designed to adaptively aggregate hierarchical features according to the feature relations within multi-scale receptive fields. Second, a graph-involved head is presented, capturing long-range channel and feature dependencies in two sub-domains. Specifically, channel dependency is acquired in a compact spatial domain for context-aware information, while the feature dependency is obtained in the graph feature domain for category-aware representation. Afterwards, by fusing the features with long-range dependencies, the network outputs the segmentation results after a learning-free upsampling layer. Experimental results present that this model remains light while achieving competitive performances in segmentation, proving the effectiveness and efficiency of the proposed sub-modules. ( https://github.com/xia-xx-cv/Graph-Lightweight-SemSeg/ ).
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