  Multiple object tracking (MOT) is the task containing detection and
association. Plenty of trackers have achieved competitive performance.
Unfortunately, for the lack of informative exchange on these subtasks, they are
often biased toward one of the two and underperform in complex scenarios, such
as the inevitable misses and mistaken trajectories of targets when tracking
individuals within a crowd. This paper proposes TransFiner, a transformer-based
approach to post-refining MOT. It is a generic attachment framework that
depends on query pairs, the bridge between an original tracker and TransFiner.
Each query pair, through the fusion decoder, produces refined detection and
motion clues for a specific object. Before that, they are feature-aligned and
group-labeled under the guidance of tracking results (locations and class
predictions) from the original tracker, finishing tracking refinement with
focus and comprehensively. Experiments show that our design is effective, on
the MOT17 benchmark, we elevate the CenterTrack from 67.8% MOTA and 64.7% IDF1
to 71.5% MOTA and 66.8% IDF1.
