论文标题
概率像素自适应改进网络
Probabilistic Pixel-Adaptive Refinement Networks
论文作者
论文摘要
编码器 - 模型网络已发现在各种密集的预测任务中广泛使用。但是，编码器中空间分辨率的强烈减少导致位置信息以及边界伪像的损失。为了解决这个问题，图像自适应后处理方法通过利用高分辨率输入图像作为指导数据显示出有益的。我们通过考虑重要的正交信息来源来扩展这种方法：网络对自己的预测的信心。我们介绍了概率像素自适应卷积（PPAC），这不仅取决于用于过滤的图像指导数据，而且还尊重每像素预测的可靠性。因此，PPAC可以使图像自适应平滑，并同时将高置信度的像素传播到较不可靠的区域，同时尊重对象边界。我们证明了它们在细化网络中的光流和语义分割中的效用，其中PPAC会导致边界伪像明显减少。此外，我们提出的改进步骤能够大大提高各种广泛使用的基准的准确性。
Encoder-decoder networks have found widespread use in various dense prediction tasks. However, the strong reduction of spatial resolution in the encoder leads to a loss of location information as well as boundary artifacts. To address this, image-adaptive post-processing methods have shown beneficial by leveraging the high-resolution input image(s) as guidance data. We extend such approaches by considering an important orthogonal source of information: the network's confidence in its own predictions. We introduce probabilistic pixel-adaptive convolutions (PPACs), which not only depend on image guidance data for filtering, but also respect the reliability of per-pixel predictions. As such, PPACs allow for image-adaptive smoothing and simultaneously propagating pixels of high confidence into less reliable regions, while respecting object boundaries. We demonstrate their utility in refinement networks for optical flow and semantic segmentation, where PPACs lead to a clear reduction in boundary artifacts. Moreover, our proposed refinement step is able to substantially improve the accuracy on various widely used benchmarks.
