# Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition

arXiv: https://arxiv.org/abs/2112.04701 · HTML: https://mlsanity.com/paper/2112.04701/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 20 of 20)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change](https://mlsanity.com/paper/2005.08135/) (2020)
- [Deep Learning Features at Scale for Visual Place Recognition](https://mlsanity.com/paper/1701.05105/) (2017)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [On the Performance of ConvNet Features for Place Recognition](https://mlsanity.com/paper/1501.04158/) (2015)
- [A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes](https://mlsanity.com/paper/1811.03032/) (2018)
- [Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods](https://mlsanity.com/paper/1903.03305/) (2019)
- [Event-based visual place recognition with ensembles of temporal windows](https://mlsanity.com/paper/2006.02826/) (2020)
- [Lightweight Unsupervised Deep Loop Closure](https://mlsanity.com/paper/1805.07703/) (2018)
- [A Cross-Season Correspondence Dataset for Robust Semantic Segmentation](https://mlsanity.com/paper/1903.06916/) (2019)
- [Hierarchical Multi-Process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2002.03895/) (2020)
- [Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion](https://mlsanity.com/paper/2010.09228/) (2020)
- [Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition](https://mlsanity.com/paper/1811.03529/) (2018)
- [Improving Visual Place Recognition Performance by Maximising Complementarity](https://mlsanity.com/paper/2102.08416/) (2021)
- [Unsupervised Learning Methods for Visual Place Recognition in Discretely and Continuously Changing Environments](https://mlsanity.com/paper/2001.08960/) (2020)
- [What makes visual place recognition easy or hard?](https://mlsanity.com/paper/2106.12671/) (2021)
- [Filter Early, Match Late: Improving Network-Based Visual Place Recognition](https://mlsanity.com/paper/1906.12176/) (2019)
- [2D Visual Place Recognition for Domestic Service Robots at Night](https://mlsanity.com/paper/1605.07708/) (2016)

## Built on by (most cited first, 6 of 6)

- [Scaling Image Geo-Localization to Continent Level](https://mlsanity.com/paper/2510.26795/) (2025)
- [Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates](https://mlsanity.com/paper/2407.08162/) (2024)
- [To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition](https://mlsanity.com/paper/2504.06116/) (2025)
- [Through the Lens of Doubt: Robust and Efficient Uncertainty Estimation for Visual Place Recognition](https://mlsanity.com/paper/2510.13464/) (2025)
- [Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2210.00834/) (2022)
- [Breaking D'ej`a Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning](https://mlsanity.com/paper/2607.12818/) (2026)

## Abstract (arXiv)

A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously. However, selecting the optimal set of techniques to use in a specific deployment environment a-priori is a difficult and unresolved challenge. Further, to the best of our knowledge, no method exists which can select a set of techniques on a frame-by-frame basis in response to image-to-image variations. In this work, we propose an unsupervised algorithm that finds the most robust set of VPR techniques to use in the current deployment environment, on a frame-by-frame basis. The selection of techniques is determined by an analysis of the similarity scores between the current query image and the collection of database images and does not require ground-truth information. We demonstrate our approach on a wide variety of datasets and VPR techniques and show that the proposed dynamic multi-process fusion (Dyn-MPF) has superior VPR performance compared to a variety of challenging competitive methods, some of which are given an unfair advantage through access to the ground-truth information.