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[arXiv]
[bibtex]@InProceedings{De_Plaen_2024_WACV, author = {De Plaen, Pierre-Fran\c{c}ois and Marinello, Nicola and Proesmans, Marc and Tuytelaars, Tinne and Van Gool, Luc}, title = {Contrastive Learning for Multi-Object Tracking With Transformers}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2024}, pages = {6867-6877} }
Contrastive Learning for Multi-Object Tracking With Transformers
Abstract
The DEtection TRansformer (DETR) opened new possibilities for object detection by modeling it as a translation task: converting image features into object-level representations. Previous works typically add expensive modules to DETR to perform Multi-Object Tracking (MOT), resulting in more complicated architectures. We instead show how DETR can be turned into a MOT model by employing an instance-level contrastive loss, a revised sampling strategy and a lightweight assignment method. Our training scheme learns object appearances while preserving detection capabilities and with little overhead. Its performance surpasses the previous state-of-the-art by +2.6 mMOTA on the challenging BDD100K dataset and is comparable to existing transformer-based methods on the MOT17 dataset.
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