论文标题
范围：光流动的动态场景范围
ScopeFlow: Dynamic Scene Scoping for Optical Flow
论文作者
论文摘要
我们建议修改光流的通用训练方案，从而导致相当大的准确性提高，而不会增加训练过程的计算复杂性。改进是基于观察当前培训协议中存在的挑战性数据的偏见，并改善了采样过程。此外，我们发现在培训方案期间，正则化和增强均应减少。 使用现有的低参数体系结构，该方法在所有其他方法中在MPI Sintel基准测试中排名第一，将最佳的两个帧方法精度提高了10％以上。该方法还超过了所有类似的体系结构变体在Kitti基准测试的基准上超过12％和19.7％，在两帧方法中，Kitti2012的平均终点错误最低，而无需使用额外的数据集。
We propose to modify the common training protocols of optical flow, leading to sizable accuracy improvements without adding to the computational complexity of the training process. The improvement is based on observing the bias in sampling challenging data that exists in the current training protocol, and improving the sampling process. In addition, we find that both regularization and augmentation should decrease during the training protocol. Using an existing low parameters architecture, the method is ranked first on the MPI Sintel benchmark among all other methods, improving the best two frames method accuracy by more than 10%. The method also surpasses all similar architecture variants by more than 12% and 19.7% on the KITTI benchmarks, achieving the lowest Average End-Point Error on KITTI2012 among two-frame methods, without using extra datasets.
