The Privacy-sensitive Object Identification (POI) task allocates bounding boxes for privacy-sensitive objects in a scene. The key to POI is settling an object's privacy class (privacy-sensitive or non-privacy-sensitive). In contrast to conventional object classes which are determined by the visual appearance of an object, one object's privacy class is derived from the scene contexts and is subject to various implicit factors beyond its visual appearance. That is, visually similar objects may be totally opposite in their privacy classes. To explicitly derive the objects' privacy class from the scene contexts, in this paper, we interpret the POI task as a visual reasoning task aimed at the privacy of each object in the scene. Following this interpretation, we propose the PrivacyGuard framework for POI. PrivacyGuard contains three stages. i) Structuring: an unstructured image is first converted into a structured, heterogeneous scene graph that embeds rich scene contexts. ii) Data Augmentation: a contextual perturbation oversampling strategy is proposed to create slightly perturbed privacy-sensitive objects in a scene graph, thereby balancing the skewed distribution of privacy classes. iii) Hybrid Graph Generation & Reasoning: the balanced, heterogeneous scene graph is then transformed into a hybrid graph by endowing it with extra "node-node" and "edge-edge" homogeneous paths. These homogeneous paths allow direct message passing between nodes or edges, thereby accelerating reasoning and facilitating the capturing of subtle context changes. Based on this hybrid graph... **For the full abstract, see the original paper.**

翻译：隐私敏感对象识别（POI）任务旨在为场景中的隐私敏感对象分配边界框。POI任务的关键在于确定对象的隐私类别（隐私敏感或非隐私敏感）。与传统对象类别由物体视觉外观决定不同，对象的隐私类别源自场景上下文，并受其视觉外观之外的各种隐含因素影响。这意味着视觉上相似的对象，其隐私类别可能完全相反。为了从场景上下文中显式推导对象的隐私类别，本文将POI任务解释为针对场景中每个对象隐私性的视觉推理任务。基于这一解释，我们提出了用于POI的PrivacyGuard框架。PrivacyGuard包含三个阶段：i) 结构化：首先将非结构化图像转换为嵌入丰富场景上下文的、结构化的异构图。ii) 数据增强：提出一种上下文扰动过采样策略，在场景图中创建轻微扰动的隐私敏感对象，从而平衡隐私类别的偏斜分布。iii) 混合图生成与推理：随后将平衡后的异构图通过赋予额外的“节点-节点”和“边-边”同质路径转化为混合图。这些同质路径允许节点或边之间直接传递信息，从而加速推理并促进捕捉细微的上下文变化。基于此混合图... **完整摘要请参见原文。**