论文标题
基于注意力的图像上升采样
Attention-based Image Upsampling
论文作者
论文摘要
卷积层是计算机视觉中许多深神经网络解决方案的组成部分。最近的工作表明，基于自我注意力的机制代替标准卷积操作会导致图像分类和对象检测任务的性能提高。在这项工作中，我们展示了如何使用注意机制来替换另一个规范的操作：跨置卷积。我们将基于新颖的基于注意的操作基于注意力集中的上采样提出来，因为它增加了特征图的空间尺寸/示例。通过对单个图像超分辨率和联合图像的实验，我们表明，基于注意力的上采样始终优于基于转移卷积或基于自适应过滤器的传统上取样方法，同时使用较少的参数。我们表明，注意机制的固有灵活性使其可以使用单独的来源来计算注意力系数和注意力目标，这使得基于注意力的上采样在融合来自多个图像模式的信息时成为自然选择。
Convolutional layers are an integral part of many deep neural network solutions in computer vision. Recent work shows that replacing the standard convolution operation with mechanisms based on self-attention leads to improved performance on image classification and object detection tasks. In this work, we show how attention mechanisms can be used to replace another canonical operation: strided transposed convolution. We term our novel attention-based operation attention-based upsampling since it increases/upsamples the spatial dimensions of the feature maps. Through experiments on single image super-resolution and joint-image upsampling tasks, we show that attention-based upsampling consistently outperforms traditional upsampling methods based on strided transposed convolution or based on adaptive filters while using fewer parameters. We show that the inherent flexibility of the attention mechanism, which allows it to use separate sources for calculating the attention coefficients and the attention targets, makes attention-based upsampling a natural choice when fusing information from multiple image modalities.
