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TransCenter: Transformers with Dense Representations for Multiple-Object Tracking. (arXiv:2103.15145v3 [cs.CV] UPDATED)
April 29, 2022, 1:10 a.m. | Yihong Xu, Yutong Ban, Guillaume Delorme, Chuang Gan, Daniela Rus, Xavier Alameda-Pineda
cs.CV updates on arXiv.org arxiv.org
Transformers have proven superior performance for a wide variety of tasks
since they were introduced, which has drawn in recent years the attention of
the vision community where efforts were made such as image classification and
object detection. Despite this wave, building an accurate and efficient
multiple-object tracking (MOT) method with transformers is not a trivial task.
We argue that the direct application of a transformer architecture with
quadratic complexity and insufficient noise-initialized sparse queries -- is
not optimal for …
More from arxiv.org / cs.CV updates on arXiv.org
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