  Video Instance Segmentation (VIS) is a multi-task problem performing
detection, segmentation, and tracking simultaneously. Extended from image set
applications, video data additionally induces the temporal information, which,
if handled appropriately, is very useful to identify and predict object
motions. In this work, we design a unified model to mutually learn these tasks.
Specifically, we propose two modules, named Temporally Correlated Instance
Segmentation (TCIS) and Bidirectional Tracking (BiTrack), to take the benefit
of the temporal correlation between the object's instance masks across adjacent
frames. On the other hand, video data is often redundant due to the frame's
overlap. Our analysis shows that this problem is particularly severe for the
YoutubeVOS-VIS2021 data. Therefore, we propose a Multi-Source Data (MSD)
training mechanism to compensate for the data deficiency. By combining these
techniques with a bag of tricks, the network performance is significantly
boosted compared to the baseline, and outperforms other methods by a
considerable margin on the YoutubeVOS-VIS 2019 and 2021 datasets.
