Tracking multiple objects based on textual queries is a challenging task that requires linking language understanding with object association across frames. Previous works typically train the whole process end-to-end or integrate an additional referring text module into a multi-object tracker, but they both require supervised training and potentially struggle with generalization to open-set queries. In this work, we introduce ReferGPT, a novel zero-shot referring multi-object tracking framework. We provide a multi-modal large language model (MLLM) with spatial knowledge enabling it to generate 3D-aware captions. This enhances its descriptive capabilities and supports a more flexible referring vocabulary without training. We also propose a robust query-matching strategy, leveraging CLIP-based semantic encoding and fuzzy matching to associate MLLM generated captions with user queries. Extensive experiments on Refer-KITTI, Refer-KITTIv2 and Refer-KITTI+ demonstrate that ReferGPT achieves competitive performance against trained methods, showcasing its robustness and zero-shot capabilities in autonomous driving. The codes are available on https://github.com/Tzoulio/ReferGPT
@article{arxiv.2504.09195,
title = {ReferGPT: Towards Zero-Shot Referring Multi-Object Tracking},
author = {Tzoulio Chamiti and Leandro Di Bella and Adrian Munteanu and Nikos Deligiannis},
journal= {arXiv preprint arXiv:2504.09195},
year = {2025}
}
Comments
Accepted CVPR 2025 Workshop on Distillation of Foundation Models for Autonomous Driving