论文标题
扩张的连续随机场进行语义分割
Dilated Continuous Random Field for Semantic Segmentation
论文作者
论文摘要
平均场近似方法学为基于语义分割的细化的现代连续随机场（CRF）解决方案奠定了基础。在本文中，我们建议通过使用建议的扩张稀疏卷积模块（DSCONV）的全局优化来放松平均场近似的硬约束 - 最大程度地降低了概率图形模型的每个节点的能量项。此外，自适应全球平均水平和自适应全球最大通用被实施，以替代完全连接的层。为了集成DSCONV，我们设计了一条端到端，时间效率扩张的管道。一元能量术语是源自前智前和柔软后功能，或使用常规分类器的预测负担映射得出的，从而更容易为各种分类器实施扩张。与其他基于CRF的方法相比，我们还提出了拟议方法的卓越实验结果。
Mean field approximation methodology has laid the foundation of modern Continuous Random Field (CRF) based solutions for the refinement of semantic segmentation. In this paper, we propose to relax the hard constraint of mean field approximation - minimizing the energy term of each node from probabilistic graphical model, by a global optimization with the proposed dilated sparse convolution module (DSConv). In addition, adaptive global average-pooling and adaptive global max-pooling are implemented as replacements of fully connected layers. In order to integrate DSConv, we design an end-to-end, time-efficient DilatedCRF pipeline. The unary energy term is derived either from pre-softmax and post-softmax features, or the predicted affordance map using a conventional classifier, making it easier to implement DilatedCRF for varieties of classifiers. We also present superior experimental results of proposed approach on the suction dataset comparing to other CRF-based approaches.
