mlsanityData to 7 Oct 2026

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

arXiv 2504.02823 ↗First posted 3 Apr 2025cs.CV eess.IVcited by 5

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

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 at https://divs1159.github.io/STING-BEE/.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 41 of its 66 references that are in the map. First posted on arXiv 3 Apr 2025.

Builds on

41 of 66 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day1 Jun

Other references · 40

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2023Visual Instruction Tuningcited by 7,792
  6. 2020End-to-End Object Detection with Transformerscited by 7,384
  7. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  8. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  9. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  10. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  11. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
  12. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
28 more
  1. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  2. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,689
  3. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,507
  4. 2021Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Conceptscited by 1,209
  5. 2021Grounded Language-Image Pre-trainingcited by 1,147
  6. 2023Kosmos-2: Grounding Multimodal Large Language Models to the Worldcited by 1,075
  7. 2023Shikra: Unleashing Multimodal LLM's Referential Dialogue Magiccited by 891
  8. 2021Florence: A New Foundation Model for Computer Visioncited by 802
  9. 2023MiniGPT-v2: large language model as a unified interface for vision-language multi-task learningcited by 617
  10. 2023Language Is Not All You Need: Aligning Perception with Language Modelscited by 543
  11. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 530
  12. 2023Ferret: Refer and Ground Anything Anywhere at Any Granularitycited by 498
  13. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 461
  14. 2023Florence-2: Advancing a Unified Representation for a Variety of Vision Taskscited by 403
  15. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensingcited by 320
  16. 2022Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Modelcited by 317
  17. 2023Self-regulating Prompts: Foundational Model Adaptation without Forgettingcited by 272
  18. 2023Vita-CLIP: Video and text adaptive CLIP via Multimodal Promptingcited by 90
  19. 2019SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Imagescited by 52
  20. 2020Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Modulecited by 39
  21. 2023Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Groundingcited by 32
  22. 2021Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmarkcited by 31
  23. 2021Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detectioncited by 30
  24. 2024MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosiscited by 28
  25. 2020Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imagingcited by 25
  26. 2022PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detectioncited by 17
  27. 2021Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognitioncited by 8
  28. 2021Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognitioncited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Can a Second-View Image Be a Language? Geometric and Semantic Cross-Modal Reasoning for X-ray Prohibited Item Detectioncited by 1
  2. 2026XSeg: A Large-scale X-ray Contraband Segmentation Benchmark For Real-World Security Screeningcited by 1
  3. 2026Falcon: Functional Assembly and Language for Compositional Reasoning in X-raycited by 1
  4. 2026OneFocus: Enabling Real-World X-ray Security Screening with a Unified Vision-Language Modelcited by 0
  5. 2026DERA: Detached Edge-Residual Adaptation for Prohibited item Detectioncited by 0

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