  Video object segmentation is challenging yet important in a wide variety of
applications for video analysis. Recent works formulate video object
segmentation as a prediction task using deep nets to achieve appealing
state-of-the-art performance. Due to the formulation as a prediction task, most
of these methods require fine-tuning during test time, such that the deep nets
memorize the appearance of the objects of interest in the given video. However,
fine-tuning is time-consuming and computationally expensive, hence the
algorithms are far from real time. To address this issue, we develop a novel
matching based algorithm for video object segmentation. In contrast to
memorization based classification techniques, the proposed approach learns to
match extracted features to a provided template without memorizing the
appearance of the objects. We validate the effectiveness and the robustness of
the proposed method on the challenging DAVIS-16, DAVIS-17, Youtube-Objects and
JumpCut datasets. Extensive results show that our method achieves comparable
performance without fine-tuning and is much more favorable in terms of
computational time.
