Flow Adaptive Video Object Segmentation.

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.