Abstract
Indoor scene classification is a critical task in computer vision, with wide-ranging applications that go from robotics to sensitive content analysis, such as child sexual abuse imagery (CSAI) classification. The problem is particularly challenging due to the intricate relationships between objects and complex spatial layouts. In this work, we propose the Attention over Scene Graphs for Sensitive Content Analysis (ASGRA), a novel framework that operates on structured graph representations instead of raw pixels. By first converting images into Scene Graphs and then employing a Graph Attention Network for inference, ASGRA directly models the interactions between a scene's components. This approach offers two key benefits: (i) inherent explainability via object and relationship identification, and (ii) privacy preservation, enabling model training without direct access to sensitive images. On Places8, we achieve 81.27% balanced accuracy, surpassing image-based methods. Real-world CSAI evaluation with law enforcement yields 74.27% balanced accuracy. Our results establish structured scene representations as a robust paradigm for indoor scene classification and CSAI classification. Code is publicly available at this https URL.
Abstract (translated)
室内场景分类是计算机视觉中的一个重要任务，其应用范围广泛，从机器人技术到敏感内容分析（如儿童性虐待图像(CSAI) 分类）都有涉及。该问题由于物体之间的复杂关系和空间布局的多样性而特别具有挑战性。在本文中，我们提出了一个新的框架——用于敏感内容分析的场景图注意力 (ASGRA)，它基于结构化的图表示而非原始像素进行操作。首先将图像转换为场景图，然后使用图注意力网络进行推理，ASGRA 直接建模场景组件之间的交互。这种方法提供了两个关键优势：(i) 通过识别对象和关系提供内在的可解释性；(ii) 保护隐私，在无需直接访问敏感图片的情况下就可以训练模型。 在 Places8 数据集上，我们达到了 81.27% 的平衡准确率，超过了基于图像的方法。与执法部门合作进行的真实世界 CSAI 测试中，我们的方法实现了 74.27% 的平衡准确率。我们的结果确立了结构化的场景表示作为一种稳健的室内场景分类和 CSAI 分类范式。代码可在以下网址公开获取：[此 URL] (请将此占位符替换为实际链接)。
URL
https://arxiv.org/abs/2509.26457
