A Semantics-Guided Warping for Semi-supervised Video Object Instance Segmentation.

In the semi-supervised video object instance segmentation domain, the mask warping technique, which warps the mask of the target object to flow vectors frame by frame, is widely used to extract target object. The big issue with this approach is that the generated warped map is not always of high accuracy, where the background or other objects may be wrongly detected as the target object. To cope with this problem, we propose to use the semantics of the target object as a guidance during the warping process. The warping confidence computation firstly judges the confidence of the generated warped map. Then a semantic selection is introduced to optimize the warped map with low confidence, where the target object is re-identified using the semantics-labels of the target object. The proposed method is assessed on the recently published large-scale Youtube-VOS dataset and compared to some state-of-the-art methods. The experimental results show that the proposed approach has a promising performance.