We present a scene parsing method that utilizes global context information
based on both the parametric and non- parametric models. Compared to previous
methods that only exploit the local relationship between objects, we train a
context network based on scene similarities to generate feature representations
for global contexts. In addition, these learned features are utilized to
generate global and spatial priors for explicit classes inference. We then
design modules to embed the feature representations and the priors into the
segmentation network as additional global context cues. We show that the
proposed method can eliminate false positives that are not compatible with the
global context representations. Experiments on both the MIT ADE20K and PASCAL
Context datasets show that the proposed method performs favorably against
existing methods.

本文提出了基于参数化和非参数化模型的全局语境信息来进行场景解析，使用场景相似性训练上下文网络生成特征表示来生成空间和全局先验知识，然后将这些特征表示和先验知识嵌入到分割网络作为额外的全局上下文提示。实验表明该方法可以消除与全局上下文表示不兼容的误报，且在 MIT ADE20K 和 PASCAL Context 数据集上表现良好。