语义分割,在机器学习上,多指对一段文本或者一张图片,提取其中有意义的部分,我们将这些有意义的部分称为语义单元,将这些语义单元提取出来的过程,称为语义分割。

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BiSeNet已被证明在实时分割two-stream网络中是有效的。但是,其添加额外path以对空间信息进行编码的原理很耗时,并且由于缺少任务专用设计,因此从预训练任务(例如图像分类)中借用的主干可能无法有效地进行图像分割。

为了解决这些问题,作者提出了一种新颖且有效的结构,即通过消除结构冗余来缓解以上的耗时问题(Short-Term Dense Concatenate network)。具体来说,本文将特征图的维数逐渐降低,并将特征图聚合起来进行图像表征,形成了STDC网络的基本模块。在decoder中,提出了一个Detail Aggregation module将空间信息的学习以single-stream方式集成到low-level layers中。最后,将low-level features和deep features融合以预测最终的分割结果。

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The balance between high accuracy and high speed has always been a challenging task in semantic image segmentation. Compact segmentation networks are more widely used in the case of limited resources, while their performances are constrained. In this paper, motivated by the residual learning and global aggregation, we propose a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions, respectively. Specifically, we propose a pixel-wise similarity distillation (PSD) module that utilizes residual attention maps to capture more detailed spatial dependencies across multiple layers. Compared with exiting methods, the PSD module greatly reduces the amount of calculation and is easy to expand. Furthermore, considering the differences in characteristics between semantic segmentation task and other computer vision tasks, we propose a category-wise similarity distillation (CSD) module, which can help the compact segmentation network strengthen the global category correlation by constructing the correlation matrix. Combining these two modules, DSD framework has no extra parameters and only a minimal increase in FLOPs. Extensive experiments on four challenging datasets, including Cityscapes, CamVid, ADE20K, and Pascal VOC 2012, show that DSD outperforms current state-of-the-art methods, proving its effectiveness and generality. The code and models will be publicly available.

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