Video Object Segmentation with Online Mask Refinement

This paper proposes a simple and effective video object instance segmentation method without fine-tuning named Mask Refinement Module(MRM). Many papers settle a labeling problem aiming to separate foreground objects, but most of them require training their networks again on target data. In a real scenario, it is not easy to collect dataset on the target environment and to label them as well due to security policies or a cost problem, especially for industry. We solve the problem by reshaping object masks with a video based online-learning method that enables us to adapt various changes frame by frame. In extensive experiments, results show that our approach is highly effective compared to modern methods by up to 13.9% improving of F-measure on large video surveillance dataset such as CDNet (118K images).