论文标题
流量主管对光流的半监督学习
Semi-Supervised Learning of Optical Flow by Flow Supervisor
论文作者
论文摘要
光流CNNS的训练管道由合成数据集的预处理阶段组成，然后在目标数据集上进行微调阶段。但是，从目标视频中获得地面真理需要巨大的努力。本文提出了一种实用的微调方法，可以将预验证的模型调整到没有地面真相流的目标数据集中，但尚未广泛探索。具体而言，我们提出了一个自我统计的流程主管，该流程由参数分离和学生量连接组成。该设计的目的是稳定的收敛性和更好的准确性，而在微调任务上是不稳定的传统自我实施方法。实验结果表明，与半监督学习的不同自学方法相比，我们方法的有效性。此外，通过利用其他未标记的数据集，我们对Sintel和Kitti基准测试的最先进的光流模型实现了有意义的改进。代码可在https://github.com/iwbn/flow-supervisor上找到。
A training pipeline for optical flow CNNs consists of a pretraining stage on a synthetic dataset followed by a fine tuning stage on a target dataset. However, obtaining ground truth flows from a target video requires a tremendous effort. This paper proposes a practical fine tuning method to adapt a pretrained model to a target dataset without ground truth flows, which has not been explored extensively. Specifically, we propose a flow supervisor for self-supervision, which consists of parameter separation and a student output connection. This design is aimed at stable convergence and better accuracy over conventional self-supervision methods which are unstable on the fine tuning task. Experimental results show the effectiveness of our method compared to different self-supervision methods for semi-supervised learning. In addition, we achieve meaningful improvements over state-of-the-art optical flow models on Sintel and KITTI benchmarks by exploiting additional unlabeled datasets. Code is available at https://github.com/iwbn/flow-supervisor.
