We propose a method for multi-object tracking and segmentation that does not require fine-tuning or per benchmark hyper-parameter selection. The proposed tracker, MeNToS, addresses particularly the data association problem. Indeed, the recently introduced HOTA metric, which 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 developed for one-shot video object segmentation to improve the association of tracklets with temporal gaps. We evaluated our tracker on KITTIMOTS and MOTSChallenge and show the benefit of our data association strategy with the HOTA metric. The project page is \url{www.mehdimiah.com/mentos+}.

翻译：我们建议一种多物体跟踪和分解方法,不需要微调或按基准超参数选择。拟议的跟踪器MenToS(MenToS)特别处理数据关联问题。事实上,最近引入的HOTA测量仪(HOTA测量仪)通过平衡检测和关联质量,更好地与人类视觉评估保持一致,它表明数据关联仍然需要改进。在利用实例分解和光学流创建跟踪器之后,拟议方法依赖于一个空间-时间存储网络,这个时间网络是为一次性视频对象分解而开发的,目的是改善跟踪器与时间差距的联系。我们评估了我们的跟踪器(KITTIMTS)和MOTS Challenge(MOTS Challenge),并展示了我们与 HOTA测量仪数据关联战略的好处。项目网页是\url{www.medimiah.com/mentos ⁇ 。