STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection

Advancements in Computer-Aided Screening (CAS) systems are essential for improving the detection of security threats in X-ray baggage scans. However, current datasets are limited in representing real-world, sophisticated threats and concealment tactics, and existing approaches are constrained by a closed-set paradigm with predefined labels. To address these challenges, we introduce STCray, the first multimodal X-ray baggage security dataset, comprising 46,642 image-caption paired scans across 21 threat categories, generated using an X-ray scanner for airport security. STCray is meticulously developed with our specialized protocol that ensures domain-aware, coherent captions, that lead to the multi-modal instruction following data in X-ray baggage security. This allows us to train a domain-aware visual AI assistant named STING-BEE that supports a range of vision-language tasks, including scene comprehension, referring threat localization, visual grounding, and visual question answering (VQA), establishing novel baselines for multi-modal learning in X-ray baggage security. Further, STING-BEE shows state-of-the-art generalization in cross-domain settings. Code, data, and models are available atthis https URL.

@article{velayudhan2025_2504.02823,
  title={ STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection },
  author={ Divya Velayudhan and Abdelfatah Ahmed and Mohamad Alansari and Neha Gour and Abderaouf Behouch and Taimur Hassan and Syed Talal Wasim and Nabil Maalej and Muzammal Naseer and Juergen Gall and Mohammed Bennamoun and Ernesto Damiani and Naoufel Werghi },
  journal={arXiv preprint arXiv:2504.02823},
  year={ 2025 }
}