# SRRM: Semantic Region Relation Model for Indoor Scene Recognition

arXiv: https://arxiv.org/abs/2305.08540 · HTML: https://mlsanity.com/paper/2305.08540/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [Training Neural Networks for and by Interpolation](https://mlsanity.com/paper/1906.05661/) (2019)
- [Semantic-Aware Scene Recognition](https://mlsanity.com/paper/1909.02410/) (2019)
- [Multi-scale recognition with DAG-CNNs](https://mlsanity.com/paper/1505.05232/) (2015)
- [Scene recognition with CNNs: objects, scales and dataset bias](https://mlsanity.com/paper/1801.06867/) (2018)
- [Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs](https://mlsanity.com/paper/1610.01119/) (2016)
- [Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition](https://mlsanity.com/paper/2108.00399/) (2021)
- [DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments](https://mlsanity.com/paper/1908.00191/) (2019)
- [BORM: Bayesian Object Relation Model for Indoor Scene Recognition](https://mlsanity.com/paper/2108.00397/) (2021)
- [Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings](https://mlsanity.com/paper/1908.06422/) (2019)

## Built on by (most cited first, 5 of 5)

- [BurstDeflicker: A Benchmark Dataset for Flicker Removal in Dynamic Scenes](https://mlsanity.com/paper/2510.09996/) (2025)
- [Inter-object Discriminative Graph Modeling for Indoor Scene Recognition](https://mlsanity.com/paper/2311.05919/) (2023)
- [Attention over Scene Graphs: Indoor Scene Representations Toward CSAI Classification](https://mlsanity.com/paper/2509.26457/) (2025)
- [Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognition](https://mlsanity.com/paper/2305.12661/) (2023)
- [Semantic-embedded Similarity Prototype for Scene Recognition](https://mlsanity.com/paper/2308.05896/) (2023)

## Abstract (arXiv)

Despite the remarkable success of convolutional neural networks in various computer vision tasks, recognizing indoor scenes still presents a significant challenge due to their complex composition. Consequently, effectively leveraging semantic information in the scene has been a key issue in advancing indoor scene recognition. Unfortunately, the accuracy of semantic segmentation has limited the effectiveness of existing approaches for leveraging semantic information. As a result, many of these approaches remain at the stage of auxiliary labeling or co-occurrence statistics, with few exploring the contextual relationships between the semantic elements directly within the scene. In this paper, we propose the Semantic Region Relationship Model (SRRM), which starts directly from the semantic information inside the scene. Specifically, SRRM adopts an adaptive and efficient approach to mitigate the negative impact of semantic ambiguity and then models the semantic region relationship to perform scene recognition. Additionally, to more comprehensively exploit the information contained in the scene, we combine the proposed SRRM with the PlacesCNN module to create the Combined Semantic Region Relation Model (CSRRM), and propose a novel information combining approach to effectively explore the complementary contents between them. CSRRM significantly outperforms the SOTA methods on the MIT Indoor 67, reduced Places365 dataset, and SUN RGB-D without retraining. The code is available at: https://github.com/ChuanxinSong/SRRM