用图注意力模型分析室内场景关系，提升敏感内容识别准确率
Attention over Scene Graphs: Indoor Scene Representations Toward CSAI Classification
- 将图像转为场景图，用图注意力网络建模物体间关系
- 在Places8数据集达81.27%准确率，真实场景下对CSAI识别达74.27%
- 兼具可解释性与隐私保护，适合敏感内容检测应用
室内场景分类是计算机视觉中的关键任务，广泛应用于机器人和敏感内容分析（如儿童性剥削影像，CSAI）识别。由于物体间复杂关系与空间布局，该任务极具挑战。本文提出基于场景图的注意力框架ASGRA，不依赖原始像素，而是先将图像转换为场景图，再通过图注意力网络进行推理，直接建模场景组件间的交互。该方法具有两大优势：(i) 通过识别物体与关系实现内在可解释性；(ii) 保护隐私，支持无需访问敏感图像即可训练模型。在Places8数据集上达到81.27%的平衡准确率，超越传统图像方法；在与执法机构合作的真实CSAI评估中，达到74.27%的平衡准确率。结果证明结构化场景表示是室内场景及CSAI分类的可靠范式。代码已公开于https://github.com/tutuzeraa/ASGRA。
原文摘要 · Abstract (English)
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 https://github.com/tutuzeraa/ASGRA.
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