A Hierarchical Loss for Semantic Segmentation
A Hierarchical Loss for Semantic Segmentation
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DOI：
10.5220/0008946002600267
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Bruce R. Muller;W. Smith
中科院分区：
文献类型：
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作者：
Bruce R. Muller;W. Smith
我们利用类层次结构的知识来帮助训练语义分割卷积神经网络。我们不修改网络本身的架构，而是建议计算一个损失，该损失是在不同级别的类抽象上的分类损失的总和。这允许网络区分严重错误（错误的超类）和轻微错误（正确的超类但不正确的finescale类），并学习属于同一超类的类之间共享的视觉特征。该方法很容易实现（我们提供了一个PyTorch实现，可以与任何现有的语义分割网络一起使用），并且我们表明，相对于使用类层次结构和相同的网络架构进行训练，它可以提高性能（更快的收敛速度，更好的平均交集）。我们提供的海伦面部和Mapillary远景道路场景分割数据集的结果。
: We exploit knowledge of class hierarchies to aid the training of semantic segmentation convolutional neural networks. We do not modify the architecture of the network itself, but rather propose to compute a loss that is a summation of classification losses at different levels of class abstraction. This allows the network to differentiate serious errors (the wrong superclass) from minor errors (correct superclass but incorrect finescale class) and to learn visual features that are shared between classes that belong to the same superclass. The method is straightforward to implement (we provide a PyTorch implementation that can be used with any existing semantic segmentation network) and we show that it yields performance improvements (faster convergence, better mean Intersection over Union) relative to training with a flat class hierarchy and the same network architecture. We provide results for the Helen facial and Mapillary Vistas road-scene segmentation datasets.