SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation

• URL: http://arxiv.org/abs/2103.10284v1
• Date: Thu, 18 Mar 2021 14:31:15 GMT
• Title: SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation
• Authors: Dongfang Liu, Yiming Cui, Wenbo Tan, Yingjie Chen
• Abstract summary: Video instance segmentation (VIS) is a new and critical task in computer vision. We propose a one-stage spatial granularity network (SG-Net) for VIS. We show that our method can achieve improved performance in both accuracy and inference speed.
• Score: 7.544917072241684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). Compared to the conventional two-stage methods, SG-Net demonstrates four advantages: 1) Our method has a one-stage compact architecture and each task head (detection, segmentation, and tracking) is crafted interdependently so they can effectively share features and enjoy the joint optimization; 2) Our mask prediction is dynamically performed on the sub-regions of each detected instance, leading to high-quality masks of fine granularity; 3) Each of our task predictions avoids using expensive proposal-based RoI features, resulting in much reduced runtime complexity per instance; 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 strong and flexible baseline for the VIS task. Our code will be available.

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