Falcon: X射线中组合推理的功能组装与语言
Falcon: Functional Assembly and Language for Compositional Reasoning in X-ray
AI总结：
针对X射线安检中威胁来自分散组件功能兼容性的问题，提出Falcon框架，通过结构化安全状态表示实现组合威胁推理，并构建Falcon-X基准验证其有效性。
摘要：
传统的视觉-语言模型大多以对象为中心，专注于检测和描述单个实体。然而，在安全关键的X射线行李筛查中，威胁往往不是来自单个物体，而是来自空间分散组件（如电池、雷管和炸药）的功能兼容性。我们将这种场景形式化为\emph{组合威胁推理}，其中风险被建模为有根据区域的关系属性，而不是独立的检测结果。我们引入了\textbf{Falcon}，一个多模态框架，将分割感知的区域特征抽象为结构化的安全状态，捕获组件存在性、成对功能兼容性和场景级风险。这种结构化表示作为显式的中间接口注入语言模型，促进关系一致且安全感知的推理。为了评估这个问题，我们提出了\textbf{Falcon-X}基准，该基准将密集接地与组件完整性和风险推理的结构化监督统一在杂乱的X射线图像中。实验表明，现有的多模态模型虽然能适应外观，但在组合安全推理方面存在困难。Falcon改进了功能接地，并产生更连贯的威胁评估，将组合安全推理确立为多模态系统的独特评估范式。
英文摘要：
Conventional vision-language models are largely object-centric, focusing on detecting and describing individual entities. In safety-critical X-ray baggage screening, however, threat often emerges not from a single object but from the functional compatibility of spatially dispersed components, such as batteries, detonators, and explosive charges. We formalize this setting as \emph{compositional threat reasoning}, where risk is modeled as a relational property of grounded regions rather than an independent detection outcome. We introduce \textbf{Falcon}, a multimodal framework that abstracts segmentation-aware region features into a structured safety state capturing component presence, pairwise functional compatibility, and scene-level risk. This structured representation is injected into the language model as an explicit intermediate interface, encouraging relationally consistent and safety-aware reasoning. To evaluate this problem, we present \textbf{Falcon-X}, a benchmark that unifies dense grounding with structured supervision over component completeness and risk inference in cluttered X-ray imagery. Experiments show that while existing multimodal models adapt to appearance, they struggle with compositional safety reasoning. Falcon improves functional grounding and produces more coherent threat assessments, establishing compositional safety reasoning as a distinct evaluation paradigm for multimodal systems.
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