# Applications of Spiking Neural Networks in Visual Place Recognition arXiv: https://arxiv.org/abs/2311.13186 ยท HTML: https://mlsanity.com/paper/2311.13186/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 73 of its 163 references that are in the map. - First posted on arXiv 22 Nov 2023. ## Builds on (most cited first, 30 of 73) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016) - [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019) - [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018) - [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015) - [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018) - [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017) - [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016) - [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021) - [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021) - [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016) - [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021) - [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019) - [Rethinking Visual Geo-localization for Large-Scale Applications](https://mlsanity.com/paper/2204.02287/) (2022) - [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023) - [MixVPR: Feature Mixing for Visual Place Recognition](https://mlsanity.com/paper/2303.02190/) (2023) - [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017) - [Deep Image Retrieval: Learning global representations for image search](https://mlsanity.com/paper/1604.01325/) (2016) - [Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks](https://mlsanity.com/paper/2207.13243/) (2022) - [Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures](https://mlsanity.com/paper/1903.06379/) (2019) - [Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks](https://mlsanity.com/paper/2303.04347/) (2023) - [TransVPR: Transformer-based place recognition with multi-level attention aggregation](https://mlsanity.com/paper/2201.02001/) (2022) - [VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change](https://mlsanity.com/paper/2005.08135/) (2020) - [Deep Model Fusion: A Survey](https://mlsanity.com/paper/2309.15698/) (2023) - [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017) - [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021) - [Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks](https://mlsanity.com/paper/2105.11654/) (2021) - [SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition](https://mlsanity.com/paper/2102.11603/) (2021) ## Built on by (most cited first, 9 of 9) - [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024) - [On Motion Blur and Deblurring in Visual Place Recognition](https://mlsanity.com/paper/2412.07751/) (2024) - [Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition](https://mlsanity.com/paper/2604.03277/) (2026) - [Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition](https://mlsanity.com/paper/2602.04401/) (2026) - [A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition](https://mlsanity.com/paper/2412.06153/) (2024) - [On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis](https://mlsanity.com/paper/2502.13191/) (2025) - [Event-driven Robust Fitting on Neuromorphic Hardware](https://mlsanity.com/paper/2508.09466/) (2025) - [LEAR: Learning Edge-Aware Representations for Event-to-LiDAR Localization](https://mlsanity.com/paper/2603.01839/) (2026) - [Visual Place Recognition Using Rate-Encoded Spiking Neural Networks with Discrete STDP Learning](https://mlsanity.com/paper/2607.13584/) (2026) ## Abstract (arXiv) In robotics, Spiking Neural Networks (SNNs) are increasingly recognized for their largely-unrealized potential energy efficiency and low latency particularly when implemented on neuromorphic hardware. Our paper highlights three advancements for SNNs in Visual Place Recognition (VPR). Firstly, we propose Modular SNNs, where each SNN represents a set of non-overlapping geographically distinct places, enabling scalable networks for large environments. Secondly, we present Ensembles of Modular SNNs, where multiple networks represent the same place, significantly enhancing accuracy compared to single-network models. Each of our Modular SNN modules is compact, comprising only 1500 neurons and 474k synapses, making them ideally suited for ensembling due to their small size. Lastly, we investigate the role of sequence matching in SNN-based VPR, a technique where consecutive images are used to refine place recognition. We demonstrate competitive performance of our method on a range of datasets, including higher responsiveness to ensembling compared to conventional VPR techniques and higher R@1 improvements with sequence matching than VPR techniques with comparable baseline performance. Our contributions highlight the viability of SNNs for VPR, offering scalable and robust solutions, and paving the way for their application in various energy-sensitive robotic tasks.