# Hierarchical Multi-Process Fusion for Visual Place Recognition

arXiv: https://arxiv.org/abs/2002.03895 · HTML: https://mlsanity.com/paper/2002.03895/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 24 of 24)

- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [TransVPR: Transformer-based place recognition with multi-level attention aggregation](https://mlsanity.com/paper/2201.02001/) (2022)
- [CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition](https://mlsanity.com/paper/2402.19231/) (2024)
- [Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition](https://mlsanity.com/paper/2402.14505/) (2024)
- [Improving Visual Place Recognition Performance by Maximising Complementarity](https://mlsanity.com/paper/2102.08416/) (2021)
- [SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition](https://mlsanity.com/paper/2502.16601/) (2025)
- [Place recognition survey: An update on deep learning approaches](https://mlsanity.com/paper/2106.10458/) (2021)
- [AANet: Aggregation and Alignment Network with Semi-hard Positive Sample Mining for Hierarchical Place Recognition](https://mlsanity.com/paper/2310.05184/) (2023)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2203.00591/) (2022)
- [Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2112.04701/) (2021)
- [Deep Homography Estimation for Visual Place Recognition](https://mlsanity.com/paper/2402.16086/) (2024)
- [Self-Supervised Learning for Place Representation Generalization across Appearance Changes](https://mlsanity.com/paper/2303.02370/) (2023)
- [Ensemble-Based Event Camera Place Recognition Under Varying Illumination](https://mlsanity.com/paper/2509.01968/) (2025)
- [Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique](https://mlsanity.com/paper/2210.07509/) (2022)
- [A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments](https://mlsanity.com/paper/2303.14247/) (2023)
- [CoPR: Towards Accurate Visual Localization With Continuous Place-descriptor Regression](https://mlsanity.com/paper/2304.07426/) (2023)
- [Region Matters: Efficient and Reliable Region-Aware Visual Place Recognition](https://mlsanity.com/paper/2604.22390/) (2026)
- [Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2210.00834/) (2022)
- [A Complementarity-Based Switch-Fuse System for Improved Visual Place Recognition](https://mlsanity.com/paper/2303.00714/) (2023)
- [An Evaluation and Ranking of Different Voting Schemes for Improved Visual Place Recognition](https://mlsanity.com/paper/2305.05705/) (2023)
- [Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments](https://mlsanity.com/paper/2401.08263/) (2024)
- [Employing Universal Voting Schemes for Improved Visual Place Recognition Performance](https://mlsanity.com/paper/2405.02297/) (2024)
- [Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition](https://mlsanity.com/paper/2510.17739/) (2025)

## Abstract (arXiv)

Combining multiple complementary techniques together has long been regarded as a way to improve performance. In visual localization, multi-sensor fusion, multi-process fusion of a single sensing modality, and even combinations of different localization techniques have been shown to result in improved performance. However, merely fusing together different localization techniques does not account for the varying performance characteristics of different localization techniques. In this paper we present a novel, hierarchical localization system that explicitly benefits from three varying characteristics of localization techniques: the distribution of their localization hypotheses, their appearance- and viewpoint-invariant properties, and the resulting differences in where in an environment each system works well and fails. We show how two techniques deployed hierarchically work better than in parallel fusion, how combining two different techniques works better than two levels of a single technique, even when the single technique has superior individual performance, and develop two and three-tier hierarchical structures that progressively improve localization performance. Finally, we develop a stacked hierarchical framework where localization hypotheses from techniques with complementary characteristics are concatenated at each layer, significantly improving retention of the correct hypothesis through to the final localization stage. Using two challenging datasets, we show the proposed system outperforming state-of-the-art techniques.