We aim to improve the performance of Multiple Object Tracking and
Segmentation (MOTS) by refinement. However, it remains challenging for refining
MOTS results, which could be attributed to that appearance features are not
adapted to target videos and it is also difficult to find proper thresholds to
discriminate them. To tackle this issue, we propose a self-supervised refining
MOTS (i.e., ReMOTS) framework. ReMOTS mainly takes four steps to refine MOTS
results from the data association perspective. (1) Training the appearance
encoder using predicted masks. (2) Associating observations across adjacent
frames to form short-term tracklets. (3) Training the appearance encoder using
short-term tracklets as reliable pseudo labels. (4) Merging short-term
tracklets to long-term tracklets utilizing adopted appearance features and
thresholds that are automatically obtained from statistical information. Using
ReMOTS, we reached the $1^{st}$ place on CVPR 2020 MOTS Challenge 1, with an
sMOTSA score of $69.9$.

提出了一种自监督的重构 MOTS 框架，并在 CVPR 2020 的 MOTS 挑战中获得了第一名，该框架利用数据关联，引入阈值，通过短期轨迹训练外观编码器，达到对 MOTS 结果的精细化改进。