  We propose a method for multi-object tracking and segmentation (MOTS) that
does not require fine-tuning or per benchmark hyperparameter selection. The
proposed method addresses particularly the data association problem. Indeed,
the recently introduced HOTA metric, that has a better alignment with the human
visual assessment by evenly balancing detections and associations quality, has
shown that improvements are still needed for data association. After creating
tracklets using instance segmentation and optical flow, the proposed method
relies on a space-time memory network (STM) developed for one-shot video object
segmentation to improve the association of tracklets with temporal gaps. To the
best of our knowledge, our method, named MeNToS, is the first to use the STM
network to track object masks for MOTS. We took the 4th place in the RobMOTS
challenge. The project page is https://mehdimiah.com/mentos.html.
