State-of-the-art models for pixel-wise prediction tasks such as image
restoration, image segmentation, or disparity estimation, involve several
stages of data resampling, in which the resolution of feature maps is first
reduced to aggregate information and then sequentially increased to generate a
high-resolution output. Several previous works have investigated the effect of
artifacts that are invoked during downsampling and diverse cures have been
proposed that facilitate to improve prediction stability and even robustness
for image classification. However, equally relevant, artifacts that arise
during upsampling have been less discussed. This is significantly relevant as
upsampling and downsampling approaches face fundamentally different challenges.
While during downsampling, aliases and artifacts can be reduced by blurring
feature maps, the emergence of fine details is crucial during upsampling.
Blurring is therefore not an option and dedicated operations need to be
considered. In this work, we are the first to explore the relevance of context
during upsampling by employing convolutional upsampling operations with
increasing kernel size while keeping the encoder unchanged. We find that
increased kernel sizes can in general improve the prediction stability in tasks
such as image restoration or image segmentation, while a block that allows for
a combination of small-size kernels for fine details and large-size kernels for
artifact removal and increased context yields the best results.

通过使用卷积上采样操作并增加内核大小而保持编码器不变，我们首次探索了上采样过程中上下文的重要性，并发现增大内核大小可以提高像素预测任务的稳定性，尤其在图像修复或图像分割任务中。