# DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments

arXiv: https://arxiv.org/abs/1908.00191 · HTML: https://mlsanity.com/paper/1908.00191/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition](https://mlsanity.com/paper/2108.00399/) (2021)
- [BORM: Bayesian Object Relation Model for Indoor Scene Recognition](https://mlsanity.com/paper/2108.00397/) (2021)
- [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)
- [Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information](https://mlsanity.com/paper/2103.06442/) (2021)
- [On the Challenges of Open World Recognitionunder Shifting Visual Domains](https://mlsanity.com/paper/2107.04461/) (2021)
- [Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition](https://mlsanity.com/paper/2201.03212/) (2022)
- [Scene Retrieval for Contextual Visual Mapping](https://mlsanity.com/paper/2102.12728/) (2021)
- [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)

In recent years, there has been a rapid increase in the number of service robots deployed for aiding people in their daily activities. Unfortunately, most of these robots require human input for training in order to do tasks in indoor environments. Successful domestic navigation often requires access to semantic information about the environment, which can be learned without human guidance. In this paper, we propose a set of DEDUCE - Diverse scEne Detection methods in Unseen Challenging Environments algorithms which incorporate deep fusion models derived from scene recognition systems and object detectors. The five methods described here have been evaluated on several popular recent image datasets, as well as real-world videos acquired through multiple mobile platforms. The final results show an improvement over the existing state-of-the-art visual place recognition systems.