论文标题
致密预测的频率和空间域的联合学习
Joint Learning of Frequency and Spatial Domains for Dense Predictions
论文作者
论文摘要
当前的人工神经网络主要在空间领域进行学习过程，但忽略了频域学习。但是，在频域中执行的学习课程比在空间域中更有效。在本文中，我们充分探索了频域学习，并提出了频率和空间域的联合学习范式。这种范式可以充分利用频率学习和空间学习的优势。具体而言，频率和空间域学习可以有效地捕获全球和本地信息。对两个密集的预测任务进行详尽的实验，即自我监督的深度估计和语义分割，表明，提议的联合学习范式可以1）在深度估计和语义分段任务中达到竞争性的性能，即使没有预告训练， 2）与其他最先进的方法相比，参数数量大大减少，这为开发现实世界应用提供了更多机会。我们希望所提出的方法可以鼓励更多的跨域学习研究。
Current artificial neural networks mainly conduct the learning process in the spatial domain but neglect the frequency domain learning. However, the learning course performed in the frequency domain can be more efficient than that in the spatial domain. In this paper, we fully explore frequency domain learning and propose a joint learning paradigm of frequency and spatial domains. This paradigm can take full advantage of the preponderances of frequency learning and spatial learning; specifically, frequency and spatial domain learning can effectively capture global and local information, respectively. Exhaustive experiments on two dense prediction tasks, i.e., self-supervised depth estimation and semantic segmentation, demonstrate that the proposed joint learning paradigm can 1) achieve performance competitive to those of state-of-the-art methods in both depth estimation and semantic segmentation tasks, even without pretraining; and 2) significantly reduce the number of parameters compared to other state-of-the-art methods, which provides more chance to develop real-world applications. We hope that the proposed method can encourage more research in cross-domain learning.
