mlsanityData to 7 Oct 2026

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

arXiv 2108.00397 ↗First posted 1 Aug 2021cs.CV cs.ROcited by 11

Not on the map. Why?

Abstract (arXiv)

Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we propose to utilize meaningful object representations for indoor scene representation. First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene. To analyze the object co-occurrences and pairwise object relations, we formulate the IOM from a Bayesian perspective as the Bayesian object relation model (BORM). Meanwhile, we incorporate the proposed BORM with the PlacesCNN model as the combined Bayesian object relation model (CBORM) for scene recognition and significantly outperforms the state-of-the-art methods on the reduced Places365 dataset, and SUN RGB-D dataset without retraining, showing the excellent generalization ability of the proposed method. Code can be found at https://github.com/hszhoushen/borm.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 13 of its 41 references that are in the map. First posted on arXiv 1 Aug 2021.

Builds on

13 of 41 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2017Mask R-CNNcited by 7,259
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  8. 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,063
  9. 2019Semantic-Aware Scene Recognitioncited by 43
  10. 2015Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networkscited by 14
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  12. 2016What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robotscited by 9
1 more
  1. 2019Scene Classification in Indoor Environments for Robots using Context Based Word Embeddingscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 2; 2025: 0; 2026: 1.

11 papers.

  1. 2022Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classificationcited by 5
  2. 2023SRRM: Semantic Region Relation Model for Indoor Scene Recognitioncited by 5
  3. 2023Inter-object Discriminative Graph Modeling for Indoor Scene Recognitioncited by 2
  4. 2023A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classificationcited by 1
  5. 2024Semantic Layering in Room Segmentation via LLMscited by 1
  6. 2022Predicting Topological Maps for Visual Navigation in Unexplored Environmentscited by 0
  7. 2023Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognitioncited by 0
  8. 2023Semantic-embedded Similarity Prototype for Scene Recognitioncited by 0
  9. 2023Class Relevance Learning For Out-of-distribution Detectioncited by 0
  10. 2024Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classificationcited by 0
  11. 2026Dynamic Graph Neural Network with Adaptive Features Selection for RGB-D Based Indoor Scene Recognitioncited by 0