Spatial attention mechanism has been widely used in semantic segmentation of
remote sensing images given its capability to model long-range dependencies.
Many methods adopting spatial attention mechanism aggregate contextual
information using direct relationships between pixels within an image, while
ignoring the scene awareness of pixels (i.e., being aware of the global context
of the scene where the pixels are located and perceiving their relative
positions). Given the observation that scene awareness benefits context
modeling with spatial correlations of ground objects, we design a scene-aware
attention module based on a refined spatial attention mechanism embedding scene
awareness. Besides, we present a local-global class attention mechanism to
address the problem that general attention mechanism introduces excessive
background noises while hardly considering the large intra-class variance in
remote sensing images. In this paper, we integrate both scene-aware and class
attentions to propose a scene-aware class attention network (SACANet) for
semantic segmentation of remote sensing images. Experimental results on three
datasets show that SACANet outperforms other state-of-the-art methods and
validate its effectiveness. Code is available at
this https URL

本文提出了一种基于场景感知注意力的空间注意力模块和局部 - 全局类注意力机制，以此构建一种应用于遥感图像语义分割的场景感知类别注意力网络（SACANet），试验表明 SACANet 的性能优于其他最先进的方法并验证了其有效性。

SACANet：用于遥感图像语义分割的场景感知类别注意力网络

SACANet: scene-aware class attention network for semantic segmentation  of remote sensing images

Most spoken language understanding systems use a pipeline approach composed
of an automatic speech recognition interface and a natural language
understanding module. This approach forces hard decisions when converting
continuous inputs into discrete language symbols. Instead, we propose a
representation model to encode speech in rich bidirectional encodings that can
be used for downstream tasks such as intent prediction. The approach uses a
masked language modelling objective to learn the representations, and thus
benefits from both the left and right contexts. We show that the performance of
the resulting encodings before fine-tuning is better than comparable models on
multiple datasets, and that fine-tuning the top layers of the representation
model improves the current state of the art on the Fluent Speech Command
dataset, also in a low-data regime, when a limited amount of labelled data is
used for training. Furthermore, we propose class attention as a spoken language
understanding module, efficient both in terms of speed and number of
parameters. Class attention can be used to visually explain the predictions of
our model, which goes a long way in understanding how the model makes
predictions. We perform experiments in English and in Dutch.

本篇研究提出了一种表示模型来编码富含双向编码的语音，同时使用遮蔽语言建模目标来学习这个模型。采用类关注作为口语理解模块，提高了模型在 Fluent Speech Command 数据集上的性能，并且可以用于可视化解释模型预测。