Attention over Scene Graphs: Indoor Scene Representations Toward CSAI Classification

• URL: http://arxiv.org/abs/2509.26457v1
• Date: Tue, 30 Sep 2025 16:09:34 GMT
• Title: Attention over Scene Graphs: Indoor Scene Representations Toward CSAI Classification
• Authors: Artur Barros, Carlos Caetano, João Macedo, Jefersson A. dos Santos, Sandra Avila,
• Abstract summary: We propose a novel framework that operates on structured graph representations instead of raw pixels.<n>On Places8, we achieve 81.27% balanced accuracy, surpassing image-based methods.<n>Our results establish structured scene representations as a robust paradigm for indoor scene classification and CSAI classification.
• Score: 3.886408092405825
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 https://github.com/tutuzeraa/ASGRA.

Related papers

• SPAN: Learning Similarity between Scene Graphs and Images with Transformers [29.582313604112336]
  We propose a Scene graPh-imAge coNtrastive learning framework, SPAN, that can measure the similarity between scene graphs and images. We introduce a novel graph serialization technique that transforms a scene graph into a sequence with structural encodings.
  arXiv  Detail & Related papers  (2023-04-02T18:13:36Z)
• Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training [112.94542676251133]
  We propose to learn scene graph embeddings by directly optimizing their alignment with images. Specifically, we pre-train an encoder to extract both global and local information from scene graphs. The resulting method, called SGDiff, allows for the semantic manipulation of generated images by modifying scene graph nodes and connections.
  arXiv  Detail & Related papers  (2022-11-21T01:11:19Z)
• SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning [61.57887011165744]
  multimodal Transformers have made great progress in the task of Visual Commonsense Reasoning. We propose a Scene Graph Enhanced Image-Text Learning framework to incorporate visual scene graphs in commonsense reasoning.
  arXiv  Detail & Related papers  (2021-12-16T03:16:30Z)
• SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification [14.016637774748677]
  Few-Shot Remote Sensing Scene Classification (FSRSSC) is an important task, which aims to recognize novel scene classes with few examples. We propose a novel scene graph matching-based meta-learning framework for FSRSSC, called SGMNet. We conduct extensive experiments on UCMerced LandUse, WHU19, AID, and NWPU-RESISC45 datasets.
  arXiv  Detail & Related papers  (2021-10-09T07:43:40Z)
• Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation [56.25878966006678]
  We propose an approach of textbfPRactical textbfInference in textbfSocial rtextbfElation (PRISE) It concisely learns interactive features of persons and discriminative features of holistic scenes. PRISE achieves 6.8$%$ improvement for domain classification in PIPA dataset.
  arXiv  Detail & Related papers  (2021-07-30T04:20:13Z)
• Segmentation-grounded Scene Graph Generation [47.34166260639392]
  We propose a framework for pixel-level segmentation-grounded scene graph generation. Our framework is agnostic to the underlying scene graph generation method. It is learned in a multi-task manner with both target and auxiliary datasets.
  arXiv  Detail & Related papers  (2021-04-29T08:54:08Z)
• ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection [101.56529337489417]
  We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
  arXiv  Detail & Related papers  (2020-08-14T09:11:18Z)
• Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation [98.34909905511061]
  We argue that a desirable scene graph should be hierarchically constructed, and introduce a new scheme for modeling scene graph. To generate a scene graph based on HET, we parse HET with a Hybrid Long Short-Term Memory (Hybrid-LSTM) which specifically encodes hierarchy and siblings context. To further prioritize key relations in the scene graph, we devise a Relation Ranking Module (RRM) to dynamically adjust their rankings.
  arXiv  Detail & Related papers  (2020-07-17T05:12:13Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.