# Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments

arXiv: https://arxiv.org/abs/2109.06452 · HTML: https://mlsanity.com/paper/2109.06452/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 56 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [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)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures](https://mlsanity.com/paper/1903.06379/) (2019)
- [VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change](https://mlsanity.com/paper/2005.08135/) (2020)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [BindsNET: A machine learning-oriented spiking neural networks library in Python](https://mlsanity.com/paper/1806.01423/) (2018)
- [Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods](https://mlsanity.com/paper/1903.03305/) (2019)
- [Event-driven Vision and Control for UAVs on a Neuromorphic Chip](https://mlsanity.com/paper/2108.03694/) (2021)
- [Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM](https://mlsanity.com/paper/1903.02504/) (2019)
- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion](https://mlsanity.com/paper/2010.09228/) (2020)
- [What makes visual place recognition easy or hard?](https://mlsanity.com/paper/2106.12671/) (2021)
- [Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs](https://mlsanity.com/paper/2004.12691/) (2020)
- [Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system](https://mlsanity.com/paper/1807.02155/) (2018)
- [Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM](https://mlsanity.com/paper/1505.04548/) (2015)

## Built on by (most cited first, 15 of 15)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Visual Place Recognition: A Tutorial](https://mlsanity.com/paper/2303.03281/) (2023)
- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition](https://mlsanity.com/paper/2209.08723/) (2022)
- [NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments](https://mlsanity.com/paper/2410.21615/) (2024)
- [Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique](https://mlsanity.com/paper/2210.07509/) (2022)
- [Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition](https://mlsanity.com/paper/2604.03277/) (2026)
- [Cross-modal Place Recognition in Image Databases using Event-based Sensors](https://mlsanity.com/paper/2307.01047/) (2023)
- [A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition](https://mlsanity.com/paper/2412.06153/) (2024)
- [Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection](https://mlsanity.com/paper/2311.09225/) (2023)
- [Hybrid guided variational autoencoder for visual place recognition](https://mlsanity.com/paper/2601.09248/) (2026)
- [Automatic Map Density Selection for Locally-Performant Visual Place Recognition](https://mlsanity.com/paper/2602.21473/) (2026)
- [Visual Place Recognition Using Rate-Encoded Spiking Neural Networks with Discrete STDP Learning](https://mlsanity.com/paper/2607.13584/) (2026)
- [Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks](https://mlsanity.com/paper/2610.16022/) (2026)

## Abstract (arXiv)

Spiking neural networks (SNNs) offer both compelling potential advantages, including energy efficiency and low latencies and challenges including the non-differentiable nature of event spikes. Much of the initial research in this area has converted deep neural networks to equivalent SNNs, but this conversion approach potentially negates some of the advantages of SNN-based approaches developed from scratch. One promising area for high-performance SNNs is template matching and image recognition. This research introduces the first high-performance SNN for the Visual Place Recognition (VPR) task: given a query image, the SNN has to find the closest match out of a list of reference images. At the core of this new system is a novel assignment scheme that implements a form of ambiguity-informed salience, by up-weighting single-place-encoding neurons and down-weighting "ambiguous" neurons that respond to multiple different reference places. In a range of experiments on the challenging Nordland, Oxford RobotCar, SPEDTest, Synthia, and St Lucia datasets, we show that our SNN achieves comparable VPR performance to state-of-the-art and classical techniques, and degrades gracefully in performance with an increasing number of reference places. Our results provide a significant milestone towards SNNs that can provide robust, energy-efficient, and low latency robot localization.