AI RESEARCH
SelfMOTR: Revisiting MOTR with Self-Generating Detection Priors
arXiv CS.CV
•
ArXi:2511.20279v2 Announce Type: replace End-to-end transformer architectures have driven significant progress in multi-object tracking by unifying detection and association into a single, heuristic-free framework. Despite these benefits, poor detection performance and the inherent conflict between detection and association in a joint architecture remain critical concerns. Recent approaches aim to mitigate these issues by employing advanced denoising or label assignment strategies, or by incorporating detection priors from external object detectors.