追寻线索：基于跨模态智能的人员重识别实验
Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence
AI总结：
提出结合大型视觉语言模型、图注意力网络与表示学习的跨模态框架cRID，通过检测文本可描述的PII线索提升人员重识别性能，在跨数据集场景中表现优异。
摘要：
作为开放数据收集和发布的街景记录，在推进自动驾驶系统和AI研究中发挥着至关重要的作用。然而，这些数据集带来了重大的隐私风险，特别是对于行人而言，因为存在超越面部等生物特征的个人身份信息（PII）。在本文中，我们提出了cRID，一种结合大型视觉语言模型、图注意力网络和表示学习的新型跨模态框架，用于检测PII的文本可描述线索并增强人员重识别（Re-ID）。我们的方法侧重于识别和利用可解释特征，从而能够检测出超越低级外观线索的语义上有意义的PII。我们对人员图像数据集中PII的存在进行了系统评估。实验表明，在实用的跨数据集Re-ID场景中性能得到提升，特别是从Market-1501到CUHK03-np（检测版），突显了该框架的实用价值。代码可在 https://github.com/RAufschlaeger/cRID 获取。
英文摘要：
The collection and release of street-level recordings as Open Data play a vital role in advancing autonomous driving systems and AI research. However, these datasets pose significant privacy risks, particularly for pedestrians, due to the presence of Personally Identifiable Information (PII) that extends beyond biometric traits such as faces. In this paper, we present cRID, a novel cross-modal framework combining Large Vision-Language Models, Graph Attention Networks, and representation learning to detect textual describable clues of PII and enhance person re-identification (Re-ID). Our approach focuses on identifying and leveraging interpretable features, enabling the detection of semantically meaningful PII beyond low-level appearance cues. We conduct a systematic evaluation of PII presence in person image datasets. Our experiments show improved performance in practical cross-dataset Re-ID scenarios, notably from Market-1501 to CUHK03-np (detected), highlighting the framework's practical utility. Code is available at https://github.com/RAufschlaeger/cRID.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
