论文标题
实时语义细分通过空间拖网引导的上下文传播
Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation
论文作者
论文摘要
如今，基于视觉的计算任务在各种现实世界应用中起着重要作用。但是，许多视觉计算任务，例如语义细分通常在计算上很昂贵，对资源受限但需要快速响应速度的计算系统提出了挑战。因此，开发仅需要有限的计算资源的准确和实时视觉处理模型是有价值的。为此，我们提出了用于实现实时语义分割的空间范围引导的上下文传播网络（SGCPNET）。在SGCPNET中，我们提出了空间 - 详细指导性上下文传播的策略。它使用浅层层的空间细节来指导低分辨率全球环境的传播，其中可以有效地重建丢失的空间信息。这样，释放了沿网络沿网络维持高分辨率功能的需求，因此很大程度上提高了模型效率。另一方面，由于有效地重建了空间细节，因此仍可以保留分割精度。在实验中，我们验证了提出的SGCPNET模型的有效性和效率。例如，在CitySACPES数据集上，我们的SGCPNET达到了69.5％MIOU分割精度，而其速度在GEFORCE GTX 1080 TI GPU卡上的768x1536图像上达到178.5 fps。另外，SGCPNET非常轻巧，仅包含0.61 M参数。
Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Citysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768x1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters.
