Convolutional neural networks (CNNs) have achieved success in optical flow estimation using labeled datasets, but they fail to build an internal representation to fast adapt to the specific task. On the other hand, limited by the lack of ground truth, existing CNNs-based methods suffer from high noise sensitivity and inferior generalization performance. We integrate the meta-learning technique with optical flow estimation, which can learn internal features to search optimal initial state parameters of the network. Meanwhile, we devise an enhanced network termed MetaFlow to further improve performance. MetaFlow extracts per-pixel features, builds correlation volumes for all pairs of pixels, and iteratively updates optical flow through optical flow predictor using meta-learning. In addition, we propose a meta-transfer pretraining approach to obtain initial network weights, which can efficiently avoid network overfitting. Empirical experiments on MPI Sintel and KITTI benchmarks have shown that the proposed MetaFlow achieves the state-of-the-art results and performs outstanding in challenging scenarios such as textureless regions and abrupt motions.

中文翻译：

---

MetaFlow：一种基于元学习的光流估计网络

卷积神经网络 (CNN) 在使用标记数据集进行光流估计方面取得了成功，但它们无法构建内部表示以快速适应特定任务。另一方面，由于缺乏基本事实，现有的基于 CNN 的方法存在高噪声敏感性和较差的泛化性能。我们将元学习技术与光流估计相结合，可以学习内部特征来搜索网络的最佳初始状态参数。同时，我们设计了一个称为 MetaFlow 的增强网络来进一步提高性能。MetaFlow 提取每像素特征，为所有像素对构建相关体积，并使用元学习通过光流预测器迭代更新光流。此外，我们提出了一种元转移预训练方法来获得初始网络权重，可以有效地避免网络过拟合。MPI Sintel 和 KITTI 基准的实证实验表明，所提出的 MetaFlow 达到了最先进的结果，并且在具有挑战性的场景（例如无纹理区域和突然运动）中表现出色。