CGATracker: Correlation-Aware Graph Alignment for Referring Multi-Object Tracking

doi:10.1109/TCSVT.2025.3576341

CGATracker: Correlation-Aware Graph Alignment for Referring Multi-Object Tracking

2025-06-04

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DOI：10.1109/TCSVT.2025.3576341

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Referring multi-object tracking (RMOT) aims to identify specific targets based on sentence descriptions. To enhance multi-modal learning, previous works typically rely on a simple fusion module at early or late stages. However, those methods frequently underutilize textual semantics and struggle to model the relationships between region-level features and word-level features. To address these limitations, we propose CGATracker, a correlation-aware graph alignment method for RMOT, which facilitates precise relationship modeling through relational scoring. Specifically, we design a Language-driven Relational Alignment (LRA) module, which establishes two connection graphs to generate positive and negative samples for the visual-textual alignment. Additionally, to effectively leverage referring information, we introduce a Semantic Clarify Booster (SCBooster) module based on a semantic infusion mechanism and a bias-aware verification mechanism for interactions with different modalities. Moreover, by designing a Multi-level Cross-modal Fusion (MCF) module, our method aggregates contextual features at multiple depths to enable the creation of the enriched correlation-aware graph. Extensive experiments conducted on the Refer-KITTI and Refer-KITTI-V2 datasets demonstrate the effectiveness of CGATracker.

Keywords:

Referring multi-object tracking

multi-modal learning

end-to-end tracking

CGATracker: Correlation-Aware Graph Alignment for Referring Multi-Object Tracking

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