Semi-Supervised Video Object Segmentation

VOS is a type of video object segmentation model consisting of two network components. The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation. The segmentation model is exclusively trained offline, designed to process the coarse scores into high quality segmentation masks.

MiVOS is a video object segmentation model which decouples interaction-to-mask and mask propagation. By decoupling interaction from propagation, MiVOS is versatile and not limited by the type of interactions. It uses three modules: Interaction-to-Mask, Propagation and Difference-Aware Fusion. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation module using a novel top-filtering strategy in reading the space-time memory. To effectively take the user's intent into account, a novel difference-aware module is proposed to learn how to properly fuse the masks before and after each interaction, which are aligned with the target frames by employing the space-time memory.

State-Aware Tracker is a pipeline for semi-supervised video object segmentation. It takes each target object as a tracklet, which not only makes the pipeline more efficient but also filters distractors to facilitate target modeling. For more stable and robust performance over video sequences, SAT gets awareness for each state and makes self-adaptation via two feedback loops. One loop assists SAT in generating more stable tracklets. The other loop helps to construct a more robust and holistic target representation.