论文标题
自我监督的自动流
Self-supervised AutoFlow
论文作者
论文摘要
最近，Autoflow在学习用于光流的训练集方面表现出了令人鼓舞的结果，但是需要目标域中的地面真实标签来计算其搜索指标。观察地面真相搜索指标与自我监督的损失之间存在很强的相关性，我们介绍了自我监管的自动流动，以处理没有地面真相标签的现实世界视频。我们使用自我监督的损失作为搜索指标，我们的自我监督的自动流动与Sintel和Kitti上的Autoflow相当，在那里可以使用地面真相，并且在现实世界中的Davis数据集上表现更好。我们在（半）有监督的环境中使用自我监督的自动流进行进一步探索，并根据最新状态获得竞争成果。
Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric. Observing a strong correlation between the ground truth search metric and self-supervised losses, we introduce self-supervised AutoFlow to handle real-world videos without ground truth labels. Using self-supervised loss as the search metric, our self-supervised AutoFlow performs on par with AutoFlow on Sintel and KITTI where ground truth is available, and performs better on the real-world DAVIS dataset. We further explore using self-supervised AutoFlow in the (semi-)supervised setting and obtain competitive results against the state of the art.
