# Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition

arXiv: https://arxiv.org/abs/2108.00399 · HTML: https://mlsanity.com/paper/2108.00399/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds](https://mlsanity.com/paper/1909.09287/) (2019)
- [Semantic-Aware Scene Recognition](https://mlsanity.com/paper/1909.02410/) (2019)
- [Scene recognition with CNNs: objects, scales and dataset bias](https://mlsanity.com/paper/1801.06867/) (2018)
- [Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks](https://mlsanity.com/paper/1509.06470/) (2015)
- [DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments](https://mlsanity.com/paper/1908.00191/) (2019)
- [What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots](https://mlsanity.com/paper/1602.00032/) (2016)
- [Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings](https://mlsanity.com/paper/1908.06422/) (2019)
- [Disentangled Relational Representations for Explaining and Learning from Demonstration](https://mlsanity.com/paper/1907.13627/) (2019)

## Built on by (most cited first, 12 of 12)

- [Spectrum-guided Multi-granularity Referring Video Object Segmentation](https://mlsanity.com/paper/2307.13537/) (2023)
- [Temporally Consistent Referring Video Object Segmentation with Hybrid Memory](https://mlsanity.com/paper/2403.19407/) (2024)
- [Referring Human Pose and Mask Estimation in the Wild](https://mlsanity.com/paper/2410.20508/) (2024)
- [Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification](https://mlsanity.com/paper/2301.00145/) (2022)
- [SRRM: Semantic Region Relation Model for Indoor Scene Recognition](https://mlsanity.com/paper/2305.08540/) (2023)
- [Inter-object Discriminative Graph Modeling for Indoor Scene Recognition](https://mlsanity.com/paper/2311.05919/) (2023)
- [Semantic Layering in Room Segmentation via LLMs](https://mlsanity.com/paper/2403.12920/) (2024)
- [Predicting Topological Maps for Visual Navigation in Unexplored Environments](https://mlsanity.com/paper/2211.12649/) (2022)
- [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)
- [Class Relevance Learning For Out-of-distribution Detection](https://mlsanity.com/paper/2401.01021/) (2023)
- [Dynamic Graph Neural Network with Adaptive Features Selection for RGB-D Based Indoor Scene Recognition](https://mlsanity.com/paper/2604.00372/) (2026)

## Abstract (arXiv)

Accurate perception of the surrounding scene is helpful for robots to make reasonable judgments and behaviours. Therefore, developing effective scene representation and recognition methods are of significant importance in robotics. Currently, a large body of research focuses on developing novel auxiliary features and networks to improve indoor scene recognition ability. However, few of them focus on directly constructing object features and relations for indoor scene recognition. In this paper, we analyze the weaknesses of current methods and propose an Object-to-Scene (OTS) method, which extracts object features and learns object relations to recognize indoor scenes. The proposed OTS first extracts object features based on the segmentation network and the proposed object feature aggregation module (OFAM). Afterwards, the object relations are calculated and the scene representation is constructed based on the proposed object attention module (OAM) and global relation aggregation module (GRAM). The final results in this work show that OTS successfully extracts object features and learns object relations from the segmentation network. Moreover, OTS outperforms the state-of-the-art methods by more than 2% on indoor scene recognition without using any additional streams. Code is publicly available at: https://github.com/FreeformRobotics/OTS.