We tackle the task of semi-supervised video object segmentation, i.e. pixel-level object classification of the images in video sequences using very limited ground truth training data of its corresponding video. We present FLow Adaptive Video Object Segmentation, an efficient pipeline based on a novel online adaptation algorithm that utilizes optical flow, capable of tracking objects effectively throughout videos. Comparing with most of the recent deep learning based approaches that trade efficiency for accuracy, we provide extensive complexity analysis and additionally demonstrate that FLAVOS is natural for real world applications by introducing an interactive pipeline that enables the user to provide feedback for online training. Our method achieves state-of-the-art accuracy on three challenging benchmark datasets and nearly ground-truth level segmentation results with interactive user feedback.

我们解决了半监督视频对象分割的任务，即使用其对应视频的非常有限的地面实况训练数据对视频序列中的图像进行像素级对象分类。我们介绍了FLow自适应视频对象分割，这是一种基于新颖的在线自适应算法的有效流水线，该算法利用光流，能够在整个视频中有效地跟踪对象。与大多数近期基于深度学习的方法（以效率换取准确性）相比，我们提供了广泛的复杂性分析，并通过引入交互式管道使用户能够为在线培训提供反馈，从而证明FLAVOS对于现实世界的应用很自然。