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Multi-layer Adaptive Feature Fusion for Semantic Segmentation

机译：用于语义分割的多层自适应特征融合

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摘要

Multi-layer feature fusion is a very important strategy for semantic segmentation, as a single-layer feature is usually unable to make an accurate prediction on every pixel. However, most current methods adopt direct summing or channel concatenation on multi-layer features, lacking of consideration of the distinction and complementarity between them. To explore their respective importance and to achieve an appropriate fusion on each pixel, in this paper, we propose a novel multi-layer adaptive feature fusion method for semantic segmentation, which is based on attention mechanism. Specifically, our method encourages the network to learn the importance of features from different layer according to the content of input image and the specific capability of each layer of feature, expressed in the form of weight map. By pixel-wisely multiplying the features with their corresponding weight maps, we can change the response values proportionally at each pixel and get several weighted features. Finally, the weighted features are summed up to obtain the highly fused feature for discrimination. A series of comparative experiments are carried out on two public datasets, PASCAL VOC 2012 and PASCAL-Person-Part, which successfully prove the effectiveness of our method. Furthermore, we visualize the weight maps of the multi-layer features to facilitate an intuitive understanding of their importance at different location.
机译：多层特征融合是语义分割的一个非常重要的策略，因为单层特征通常无法对每个像素进行准确的预测。然而，大多数当前方法采用了多层特征的直接求和或通道连接，缺乏考虑它们之间的区别和互补性。为了探讨各自的重要性并在每个像素上实现适当的融合，本文提出了一种用于语义分割的新型多层自适应特征融合方法，其基于注意机制。具体地，我们的方法鼓励网络根据输入图像的内容和每层特征的特定能力来学习来自不同层的特征的重要性，以重量图的形式表示。通过像素 - 明智地将特征乘以其对应的权重映射，我们可以在每个像素处成比例地改变响应值并获得多个加权特征。最后，总结加权特征以获得高度融合的歧视特征。一系列比较实验是在两个公共数据集，Pascal Voc 2012和Pascal-Person部分进行的，该部分成功证明了我们方法的有效性。此外，我们可视化多层特征的权重映射，以促进对不同位置的重要性的直观理解。

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