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.**

隐私敏感目标识别任务通过为场景中的隐私敏感目标分配边界框来确定对象的隐私类别，并将该任务解释为场景中每个对象的隐私视角的视觉推理任务；因此，本文提出了一种名为 PrivacyGuard 的框架来处理此任务，该框架包含了三个核心阶段：结构化、数据增强和混合图生成与推理，通过这些阶段，可以改善场景的推理能力，并捕捉到微妙的上下文变化。