SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation
Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granularity network (SG-Net). SG-Net demonstrates four advantages: 1) Our task heads (detection, segmentation, and tracking) are crafted interdependently so they can effectively share features and enjoy the joint optimization; 2) Each of our task predictions avoids using proposal-based RoI features, resulting in much reduced runtime complexity per instance; 3) Our mask prediction is dynamically performed on the sub-regions of each detected instance, leading to high-quality masks of fine granularity; 4) Our tracking head models objects' centerness movements for tracking, which effectively enhances the tracking robustness to different object appearances. In evaluation, we present state-of-the-art comparisons on the YouTube-VIS dataset. Extensive experiments demonstrate that our compact one-stage method can achieve improved performance in both accuracy and inference speed. We hope our SG-Net could serve as a simple yet strong baseline for the VIS task. Code will be available.