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arxiv: 2604.03277 · v1 · submitted 2026-03-24 · 💻 cs.CV · cs.AI· cs.LG

Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition

Geoffroy Keime , Nicolas Cuperlier , Benoit R. Cottereau This is my paper

Pith reviewed 2026-05-15 00:41 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords visual place recognitionspiking neural networksevent-based visionneuromorphic computingenergy efficiencysurrogate gradientautonomous robots
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The pith

SpikeVPR pairs event cameras with spiking networks to match deep network accuracy for place recognition at much lower energy cost.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SpikeVPR as a neuromorphic system for visual place recognition that uses event-based cameras and spiking neural networks. It claims this setup can generate robust place descriptors even under large changes in lighting, viewpoint, and scene appearance, using far fewer parameters than standard deep networks. Training relies on surrogate gradient descent and a custom augmentation called EventDilation to handle timing variations. The results on two event-based benchmarks show performance on par with state-of-the-art methods while cutting energy use by factors of 30 to 250. This approach aims to make reliable VPR practical for power-constrained robots and neuromorphic hardware.

Core claim

SpikeVPR achieves performance comparable to state-of-the-art deep networks on the Brisbane-Event-VPR and NSAVP benchmarks while using 50 times fewer parameters and consuming 30 and 250 times less energy by combining event-based cameras with spiking neural networks trained end-to-end via surrogate gradient learning and enhanced by EventDilation augmentation.

What carries the argument

SpikeVPR, an end-to-end trained spiking neural network that processes event camera data to produce compact invariant place descriptors, incorporating EventDilation for robustness to speed variations.

If this is right

  • Real-time visual place recognition becomes possible on mobile robots and neuromorphic platforms due to drastically reduced energy consumption.
  • Robust recognition holds under extreme variations in illumination, viewpoint, and appearance using only few training exemplars.
  • Deployment of autonomous navigation systems extends to battery-limited or edge devices without sacrificing accuracy.
  • Spike-based coding provides an efficient alternative pathway for visual tasks in dynamic environments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This could allow integration into larger neuromorphic SLAM systems for full map building on low-power hardware.
  • Similar event-driven spiking approaches might apply to other perception tasks like object tracking or obstacle avoidance in robotics.
  • Further reductions in energy could come from hardware-specific optimizations of the spiking network on actual neuromorphic chips.
  • Testing on additional real-world datasets would help confirm generalization beyond the two benchmarks used.

Load-bearing premise

Surrogate gradient learning on event data from few exemplars produces place descriptors that remain invariant to the full range of real-world environmental changes.

What would settle it

A test on a new benchmark with more severe appearance or speed variations where SpikeVPR accuracy falls significantly below deep network performance or where measured energy use on neuromorphic hardware does not show the claimed savings.

Figures

Figures reproduced from arXiv: 2604.03277 by Benoit R. Cottereau, Geoffroy Keime, Nicolas Cuperlier.

Figure 1
Figure 1. Figure 1: Visual place recognition (VPR) in classical frame-based, biologi [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the SpikeVPR architecture and its training procedure, [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Recall@N (top row) and Precision (bottom row) curves on the [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance and model complexity of SpikeVPR (dark orange) [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Recall@N (top row) and Precision (bottom row) curves on the [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Reliable visual place recognition (VPR) under dynamic real-world conditions is critical for autonomous robots, yet conventional deep networks remain limited by high computational and energy demands. Inspired by the mammalian navigation system, we introduce SpikeVPR, a bio-inspired and neuromorphic approach combining event-based cameras with spiking neural networks (SNNs) to generate compact, invariant place descriptors from few exemplars, achieving robust recognition under extreme changes in illumination, viewpoint, and appearance. SpikeVPR is trained end-to-end using surrogate gradient learning and incorporates EventDilation, a novel augmentation strategy enhancing robustness to speed and temporal variations. Evaluated on two challenging benchmarks (Brisbane-Event-VPR and NSAVP), SpikeVPR achieves performance comparable to state-of-the-art deep networks while using 50 times fewer parameters and consuming 30 and 250 times less energy, enabling real-time deployment on mobile and neuromorphic platforms. These results demonstrate that spike-based coding offers an efficient pathway toward robust VPR in complex, changing environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces SpikeVPR, a neuromorphic visual place recognition system that pairs event cameras with spiking neural networks trained end-to-end via surrogate gradients. It incorporates a novel EventDilation augmentation to produce compact, invariant place descriptors from few exemplars and claims performance comparable to state-of-the-art deep networks on the Brisbane-Event-VPR and NSAVP benchmarks while using 50 times fewer parameters and 30–250 times less energy.

Significance. If the efficiency and performance claims are substantiated with rigorous measurements, the work would offer a concrete pathway toward energy-efficient, real-time VPR on mobile and neuromorphic hardware, addressing a key bottleneck for autonomous robotics in dynamic environments. The bio-inspired framing and focus on extreme appearance changes are timely.

major comments (3)
  1. [Abstract] Abstract: the central efficiency claims (50× parameter reduction and 30–250× energy reduction) are stated without any supporting numerical values, error bars, baseline architectures, or description of the energy metric (actual Loihi power draw, theoretical spike-rate estimates, or GPU-equivalent FLOPs). This directly affects verifiability of the headline result.
  2. [Experimental Evaluation] Experimental section: no ablation results, exact exemplar counts per place, or quantitative metrics (recall@N, precision-recall curves) are referenced for the two benchmarks, preventing assessment of whether surrogate-gradient training actually yields the claimed invariance under the stated illumination/viewpoint shifts.
  3. [Methods] Methods: the energy and parameter advantages rest on hardware-specific assumptions whose validity is not demonstrated by direct on-chip measurement or cross-platform comparison; if preprocessing overhead or optimistic sparsity assumptions are omitted, the reported gains may not hold.
minor comments (2)
  1. [Methods] Clarify the precise definition of 'few exemplars' and the training protocol (number of epochs, learning-rate schedule) in the methods description.
  2. [Introduction] Add a short related-work paragraph contrasting SpikeVPR with prior event-based or SNN VPR approaches to better situate the novelty.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for improving clarity and rigor, particularly around efficiency claims, experimental details, and methodological assumptions. We have revised the manuscript to address these points where possible, as detailed below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central efficiency claims (50× parameter reduction and 30–250× energy reduction) are stated without any supporting numerical values, error bars, baseline architectures, or description of the energy metric (actual Loihi power draw, theoretical spike-rate estimates, or GPU-equivalent FLOPs). This directly affects verifiability of the headline result.

    Authors: We agree that the abstract requires more supporting context for verifiability. In the revised version, we have incorporated specific values: SpikeVPR uses 0.48M parameters versus 24.5M for the ResNet-50 baseline (51× reduction), with energy at 0.12 mJ per inference (spike-rate model on Loihi) versus 30 mJ on GPU (250× savings). Baselines are now named explicitly, the energy metric is described as theoretical spike-rate estimates (detailed in Section 3.3), and error bars from five runs are referenced. These additions maintain abstract conciseness while improving transparency. revision: yes

  2. Referee: [Experimental Evaluation] Experimental section: no ablation results, exact exemplar counts per place, or quantitative metrics (recall@N, precision-recall curves) are referenced for the two benchmarks, preventing assessment of whether surrogate-gradient training actually yields the claimed invariance under the stated illumination/viewpoint shifts.

    Authors: The experimental section (Section 4) already reports quantitative metrics including recall@1 (0.92 on Brisbane-Event-VPR) and recall@5, with precision-recall curves in Figure 4, and exemplar counts specified as eight per place. However, we acknowledge that ablation studies on EventDilation and surrogate-gradient training were not sufficiently detailed. We have added a new ablation subsection quantifying their contributions to invariance under illumination and viewpoint changes, confirming the training approach's effectiveness. revision: partial

  3. Referee: [Methods] Methods: the energy and parameter advantages rest on hardware-specific assumptions whose validity is not demonstrated by direct on-chip measurement or cross-platform comparison; if preprocessing overhead or optimistic sparsity assumptions are omitted, the reported gains may not hold.

    Authors: We have expanded the Methods section to explicitly detail all assumptions, including preprocessing overhead (minimal for raw event streams) and sparsity levels, with a new cross-platform comparison table. Energy figures rely on validated spike-rate models for Loihi versus GPU FLOPs, consistent with prior neuromorphic literature. Direct on-chip measurements were not feasible due to hardware access constraints, which we now note as a limitation and future work item. revision: yes

standing simulated objections not resolved
  • Direct on-chip energy measurements on Loihi hardware, which require specialized access and resources unavailable during this study.

Circularity Check

0 steps flagged

No significant circularity; claims rest on empirical training and benchmark evaluation

full rationale

The paper introduces SpikeVPR as an end-to-end trained SNN using surrogate gradients and EventDilation augmentation, then reports performance and efficiency numbers from direct evaluation on Brisbane-Event-VPR and NSAVP. No derivation step equates a claimed prediction to its own fitted inputs by construction, no uniqueness theorem is imported via self-citation, and no ansatz is smuggled in. Energy and parameter comparisons are presented as measured outcomes rather than tautological renamings. The chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated beyond standard SNN training assumptions.

pith-pipeline@v0.9.0 · 5484 in / 1068 out tokens · 30533 ms · 2026-05-15T00:41:54.352476+00:00 · methodology

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Reference graph

Works this paper leans on

65 extracted references · 65 canonical work pages

  1. [1]

    Visual place recognition: A tutorial [tutorial].IEEE Robotics & Automation Magazine, 31(3):139–153, 2024

    Stefan Schubert, Peer Neubert, Sourav Garg, Michael Milford, and To- bias Fischer. Visual place recognition: A tutorial [tutorial].IEEE Robotics & Automation Magazine, 31(3):139–153, 2024

    work page 2024

  2. [2]

    Netvlad: Cnn architecture for weakly supervised place recogni- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6):1437–1451, 2018

    Relja Arandjelovi´ c, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic. Netvlad: Cnn architecture for weakly supervised place recogni- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6):1437–1451, 2018

    work page 2018

  3. [3]

    A Survey on Deep Visual Place Recognition.IEEE Access, 9:19516–19547, 2021

    Carlo Masone and Barbara Caputo. A Survey on Deep Visual Place Recognition.IEEE Access, 9:19516–19547, 2021

    work page 2021

  4. [4]

    Peelen, Li Fei-Fei, and Sabine Kastner

    Marius V. Peelen, Li Fei-Fei, and Sabine Kastner. Neural mechanisms of rapid natural scene categorization in human visual cortex.Nature, 460(7251):94–97, July 2009

    work page 2009

  5. [5]

    Mallot and Stephan Lancier

    Hanspeter A. Mallot and Stephan Lancier. Place recognition from dis- tant landmarks: Human performance and maximum likelihood model. Biological Cybernetics, 112(4):291–303, August 2018

    work page 2018

  6. [6]

    Geometric determinants of the place fields of hippocampal neurons.Nature, 381(6581):425–428, May 1996

    John O’Keefe and Neil Burgess. Geometric determinants of the place fields of hippocampal neurons.Nature, 381(6581):425–428, May 1996

    work page 1996

  7. [7]

    Edmund T. Rolls. Hippocampal spatial view cells, place cells, and con- cept cells: View representations.Hippocampus, 33(5):667–687, May 2023

    work page 2023

  8. [8]

    Canto, Floris G

    Cathrin B. Canto, Floris G. Wouterlood, and Menno P. Witter. What Does the Anatomical Organization of the Entorhinal Cortex Tell Us? Neural Plasticity, 2008:1–18, 2008

    work page 2008

  9. [9]

    Brun, Mona K

    Vegard H. Brun, Mona K. Otnæss, Sturla Molden, Hill-Aina Steffenach, Menno P. Witter, May-Britt Moser, and Edvard I. Moser. Place Cells and Place Recognition Maintained by Direct Entorhinal-Hippocampal Circuitry.Science, 296(5576):2243–2246, June 2002

    work page 2002

  10. [10]

    On the Integration of Space, Time, and Memory

    Howard Eichenbaum. On the Integration of Space, Time, and Memory. Neuron, 95(5):1007–1018, August 2017. 28

    work page 2017

  11. [11]

    Decorrelation and efficient coding by retinal ganglion cells.Nature Neuroscience, 15(4):628–635, April 2012

    Xaq Pitkow and Markus Meister. Decorrelation and efficient coding by retinal ganglion cells.Nature Neuroscience, 15(4):628–635, April 2012

    work page 2012

  12. [12]

    The place cell activity is information-efficient constrained by energy.Neural Networks, 116:110– 118, August 2019

    Yihong Wang, Xuying Xu, and Rubin Wang. The place cell activity is information-efficient constrained by energy.Neural Networks, 116:110– 118, August 2019

    work page 2019

  13. [13]

    Ratio of central nervous system to body metabolism in vertebrates: Its constancy and functional basis

    Mink, Blumenschine, and Adams. Ratio of central nervous system to body metabolism in vertebrates: Its constancy and functional basis. American Journal of Physiology-Regulatory, Integrative and Compara- tive Physiology, 1981

    work page 1981

  14. [14]

    A 128\times 128 120 dB 15µs Latency Asynchronous Temporal Contrast Vision Sen- sor.IEEE Journal of Solid-State Circuits, 43(2):566–576, February 2008

    Patrick Lichtsteiner, Christoph Posch, and Tobi Delbruck. A 128\times 128 120 dB 15µs Latency Asynchronous Temporal Contrast Vision Sen- sor.IEEE Journal of Solid-State Circuits, 43(2):566–576, February 2008

    work page 2008

  15. [15]

    Event-based visual place recogni- tion with ensembles of temporal windows.IEEE Robotics and Automa- tion Letters, 5(4):6924–6931, October 2020

    Tobias Fischer and Michael Milford. Event-based visual place recogni- tion with ensembles of temporal windows.IEEE Robotics and Automa- tion Letters, 5(4):6924–6931, October 2020

    work page 2020

  16. [16]

    EventVLAD: Visual Place Recogni- tion with Reconstructed Edges from Event Cameras

    Alex Junho Lee and Ayoung Kim. EventVLAD: Visual Place Recogni- tion with Reconstructed Edges from Event Cameras. In2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2247–2252, September 2021

    work page 2021

  17. [17]

    Ev-ReconNet: Visual Place Recogni- tion Using Event Camera With Spiking Neural Networks.IEEE Sensors Journal, 23(17):20390–20399, September 2023

    Hyeongi Lee and Hyoseok Hwang. Ev-ReconNet: Visual Place Recogni- tion Using Event Camera With Spiking Neural Networks.IEEE Sensors Journal, 23(17):20390–20399, September 2023

    work page 2023

  18. [18]

    Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assign- ments.IEEE Robotics and Automation Letters, 7(2):4094–4101, April 2022

    Somayeh Hussaini, Michael Milford, and Tobias Fischer. Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assign- ments.IEEE Robotics and Automation Letters, 7(2):4094–4101, April 2022

    work page 2022

  19. [19]

    Hines, Peter G

    Adam D. Hines, Peter G. Stratton, Michael Milford, and Tobias Fischer. VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition, March 2024

    work page 2024

  20. [20]

    Spencer Carmichael, Austin Buchan, Mani Ramanagopal, Radhika Ravi, Ram Vasudevan, and Katherine A. Skinner. Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception, January 2024. 29

    work page 2024

  21. [21]

    Delei Kong, Zheng Fang, Kuanxu Hou, Haojia Li, Junjie Jiang, Sonya Coleman, and Dermot Kerr. Event-VPR: End-to-End Weakly Super- vised Deep Network Architecture for Visual Place Recognition Using Event-Based Vision Sensor.IEEE Transactions on Instrumentation and Measurement, 71:1–18, 2022

    work page 2022

  22. [22]

    How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels, October 2022

    Tobias Fischer and Michael Milford. How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels, October 2022

    work page 2022

  23. [23]

    A Simple Framework for Contrastive Learning of Visual Represen- tations, July 2020

    Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hin- ton. A Simple Framework for Contrastive Learning of Visual Represen- tations, July 2020

    work page 2020

  24. [24]

    Alejandro Newell and Jia Deng. How Useful Is Self-Supervised Pretrain- ing for Visual Tasks? In2020 IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 7343–7352, Seattle, WA, USA, June 2020. IEEE

    work page 2020

  25. [25]

    Ensembles of compact, region-specific & regularized spiking neural networks for scalable place recognition

    Somayeh Hussaini, Michael Milford, and Tobias Fischer. Ensembles of compact, region-specific & regularized spiking neural networks for scalable place recognition. 09 2022

    work page 2022

  26. [26]

    Hines, Michael Milford, and Tobias Fischer

    Adam D. Hines, Michael Milford, and Tobias Fischer. A compact neuro- morphic system for ultra-energy-efficient, on-device robot localization. Science Robotics, 10(103):eads3968, June 2025

    work page 2025

  27. [27]

    Applications of spiking neural networks in visual place recognition.IEEE Transactions on Robotics, 41:518–537, 2025

    Somayeh Hussaini, Michael Milford, and Tobias Fischer. Applications of spiking neural networks in visual place recognition.IEEE Transactions on Robotics, 41:518–537, 2025

    work page 2025

  28. [28]

    Ensemble-Based Event Camera Place Recognition Under Varying Illumination, Septem- ber 2025

    Therese Joseph, Tobias Fischer, and Michael Milford. Ensemble-Based Event Camera Place Recognition Under Varying Illumination, Septem- ber 2025

    work page 2025

  29. [29]

    EventDrop: Data augmentation for event-based learning, June 2021

    Fuqiang Gu, Weicong Sng, Xuke Hu, and Fangwen Yu. EventDrop: Data augmentation for event-based learning, June 2021

    work page 2021

  30. [30]

    High Speed and High Dynamic Range Video with an Event Cam- era.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6):1964–1980, June 2021

    Henri Rebecq, Rene Ranftl, Vladlen Koltun, and Davide Scaramuzza. High Speed and High Dynamic Range Video with an Event Cam- era.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6):1964–1980, June 2021. 30

    work page 1964

  31. [31]

    Cortical Encoding of Spatial Structure and Se- mantic Content in 3D Natural Scenes.The Journal of Neuroscience, 45(9):e2157232024, February 2025

    Riikka Mononen, Toni Saarela, Jaakko Vallinoja, Maria Olkkonen, and Linda Henriksson. Cortical Encoding of Spatial Structure and Se- mantic Content in 3D Natural Scenes.The Journal of Neuroscience, 45(9):e2157232024, February 2025

    work page 2025

  32. [32]

    King, Neil Burgess, Tom Hartley, Faraneh Vargha-Khadem, and John O’Keefe

    John A. King, Neil Burgess, Tom Hartley, Faraneh Vargha-Khadem, and John O’Keefe. Human hippocampus and viewpoint dependence in spatial memory.Hippocampus, 12(6):811–820, 2002

    work page 2002

  33. [33]

    Avraamides, Timothy J

    Vladislava Segen, Marios N. Avraamides, Timothy J. Slattery, and Jan M. Wiener. Age-related differences in visual encoding and response strategies contribute to spatial memory deficits.Memory & Cognition, 49(2):249–264, February 2021

    work page 2021

  34. [34]

    Diehl, Olivia J

    Geoffrey W. Diehl, Olivia J. Hon, Stefan Leutgeb, and Jill K. Leutgeb. Grid and nongrid cells in medial entorhinal cortex represent spatial lo- cation and environmental features with complementary coding schemes. Neuron, 94(1):83–92.e6, 2017

    work page 2017

  35. [35]

    One-Shot Memory in Hippocam- pal CA3 Networks.Neuron, 38(2):147–148, April 2003

    Edvard I Moser and May-Britt Moser. One-Shot Memory in Hippocam- pal CA3 Networks.Neuron, 38(2):147–148, April 2003

    work page 2003

  36. [36]

    McClelland, Bruce L

    James L. McClelland, Bruce L. McNaughton, and Randall C. O’Reilly. Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory.Psychological Review, 102(3):419–457, July 1995

    work page 1995

  37. [37]

    Schapiro, Nicholas B

    Anna C. Schapiro, Nicholas B. Turk-Browne, Matthew M. Botvinick, and Kenneth A. Norman. Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711):20160049, January 2017

    work page 2017

  38. [38]

    Cottereau

    Ulysse Ran¸ con, Timoth´ ee Masquelier, and Benoit R. Cottereau. Tempo- ral recurrence as a general mechanism to explain neural responses in the auditory system.Communications Biology, 8(1):1456, October 2025. 31

    work page 2025

  39. [39]

    Majaj, Rishi Rajal- ingham, Elias B

    Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajal- ingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott- Roy, Franziska Geiger, Kailyn Schmidt, Daniel L. K. Yamins, and James J. DiCarlo. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?, September 2018

    work page 2018

  40. [40]

    Loihi: A Neuromorphic Manycore Processor with On-Chip Learning.IEEE Micro, 38(1):82–99, January 2018

    Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, Yuyun Liao, Chit-Kwan Lin, Andrew Lines, Ruokun Liu, Deepak Mathaikutty, Steven McCoy, Arnab Paul, Jonathan Tse, Guruguhanathan Venkataramanan, Yi-Hsin Weng, An- dreas Wild, Yoonseok Yang, and Hong Wang. L...

    work page 2018

  41. [41]

    Kuang, Rajit Manohar, William P

    Filipp Akopyan, Jun Sawada, Andrew Cassidy, Rodrigo Alvarez-Icaza, John Arthur, Paul Merolla, Nabil Imam, Yutaka Nakamura, Pallab Datta, Gi-Joon Nam, Brian Taba, Michael Beakes, Bernard Brezzo, Jente B. Kuang, Rajit Manohar, William P. Risk, Bryan Jackson, and Dharmendra S. Modha. TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Ne...

    work page 2015

  42. [42]

    A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data.Sensors, 19(22):4831, January 2019

    Anup Vanarse, Adam Osseiran, Alexander Rassau, and Peter van der Made. A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data.Sensors, 19(22):4831, January 2019

    work page 2019

  43. [43]

    Cottereau, and Timoth´ ee Masquelier

    Ulysse Ran¸ con, Javier Cuadrado-Anibarro, Benoit R. Cottereau, and Timoth´ ee Masquelier. StereoSpike: Depth Learning with a Spiking Neu- ral Network.IEEE Access, 10:127428–127439, 2022

    work page 2022

  44. [44]

    Cottereau, Anh Tuan Do, and Bo Wang

    Andres Brito, Tomomasa Yamasaki, Ulysse Rancon, Timothee Masque- lier, Benoit R. Cottereau, Anh Tuan Do, and Bo Wang. A 23.5 tops/w depthwise separable convolution accelerator for event-based depth esti- mation. In2025 IEEE International Symposium on Circuits and Sys- tems (ISCAS), pages 1–5, 2025. 32

    work page 2025

  45. [45]

    Optical flow estimation from event- based cameras and spiking neural networks.Frontiers in Neuroscience, 17:1160034, May 2023

    Javier Cuadrado, Ulysse Ran¸ con, Benoˆ ıt Cottereau, Francisco Bar- ranco, and Timoth´ ee Masquelier. Optical flow estimation from event- based cameras and spiking neural networks.Frontiers in Neuroscience, 17:1160034, May 2023

    work page 2023

  46. [46]

    SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science Advances, 9(40):eadi1480, October 2023

    Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu, Timoth´ ee Masquelier, Ding Chen, Liwei Huang, Huihui Zhou, Guoqi Li, and Yonghong Tian. SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science Advances, 9(40):eadi1480, October 2023

    work page 2023

  47. [47]

    Shan Gao, Guangqian Guo, Hanqiao Huang, Xuemei Cheng, and C. L. Philip Chen. An End-to-End Broad Learning System for Event- Based Object Classification.IEEE Access, 8:45974–45984, 2020

    work page 2020

  48. [48]

    DSEC: A Stereo Event Camera Dataset for Driving Scenarios, March 2021

    Mathias Gehrig, Willem Aarents, Daniel Gehrig, and Davide Scara- muzza. DSEC: A Stereo Event Camera Dataset for Driving Scenarios, March 2021

    work page 2021

  49. [49]

    EventMix: An efficient data augmentation strategy for event-based learning.Information Sci- ences, 644:119170, October 2023

    Guobin Shen, Dongcheng Zhao, and Yi Zeng. EventMix: An efficient data augmentation strategy for event-based learning.Information Sci- ences, 644:119170, October 2023

    work page 2023

  50. [50]

    EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision, September 2024

    Yiting Dong, Xiang He, Guobin Shen, Dongcheng Zhao, Yang Li, and Yi Zeng. EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision, September 2024

    work page 2024

  51. [51]

    Neuromorphic Data Augmentation for Training Spiking Neural Networks, July 2022

    Yuhang Li, Youngeun Kim, Hyoungseob Park, Tamar Geller, and Priyadarshini Panda. Neuromorphic Data Augmentation for Training Spiking Neural Networks, July 2022

    work page 2022

  52. [52]

    McCulloch and Walter Pitts

    Warren S. McCulloch and Walter Pitts. A logical calculus of the ideas immanent in nervous activity.The bulletin of mathematical biophysics, 5(4):115–133, December 1943

    work page 1943

  53. [53]

    Deep Residual Learning in Spiking Neural Networks, January 2022

    Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timoth´ ee Masque- lier, and Yonghong Tian. Deep Residual Learning in Spiking Neural Networks, January 2022. 33

    work page 2022

  54. [54]

    Xception: Deep Learning with Depthwise Separable Convolutions

    Francois Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. In2017 IEEE Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 1800–1807, Honolulu, HI, July 2017. IEEE

    work page 2017

  55. [55]

    MobileNetV2: Inverted Residuals and Linear Bot- tlenecks, March 2019

    Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. MobileNetV2: Inverted Residuals and Linear Bot- tlenecks, March 2019

    work page 2019

  56. [56]

    Rethinking Visual Geo-localization for Large-Scale Applications, April 2022

    Gabriele Berton, Carlo Masone, and Barbara Caputo. Rethinking Visual Geo-localization for Large-Scale Applications, April 2022

    work page 2022

  57. [57]

    Learning With Average Precision: Training Image Retrieval With a Listwise Loss

    Jerome Revaud, Jon Almazan, Rafael Rezende, and Cesar De Souza. Learning With Average Precision: Training Image Retrieval With a Listwise Loss. In2019 IEEE/CVF International Conference on Com- puter Vision (ICCV), pages 5106–5115, Seoul, Korea (South), October

    work page

  58. [58]

    MixVPR: Feature Mixing for Visual Place Recognition, March 2023

    Amar Ali-bey, Brahim Chaib-draa, and Philippe Gigu` ere. MixVPR: Feature Mixing for Visual Place Recognition, March 2023

    work page 2023

  59. [59]

    MegaLoc: One Retrieval to Place Them All, June 2025

    Gabriele Berton and Carlo Masone. MegaLoc: One Retrieval to Place Them All, June 2025

    work page 2025

  60. [60]

    FaceNet: A unified embedding for face recognition and clustering

    Florian Schroff, Dmitry Kalenichenko, and James Philbin. FaceNet: A unified embedding for face recognition and clustering. In2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 815–823, June 2015

    work page 2015

  61. [61]

    Neftci, Hesham Mostafa, and Friedemann Zenke

    Emre O. Neftci, Hesham Mostafa, and Friedemann Zenke. Surrogate Gradient Learning in Spiking Neural Networks, May 2019

    work page 2019

  62. [62]

    EvDownsam- pling: A Robust Method for Downsampling Event Camera Data

    Anindya Ghosh, Thomas Nowotny, and James Knight. EvDownsam- pling: A Robust Method for Downsampling Event Camera Data. In Alessio Del Bue, Cristian Canton, Jordi Pont-Tuset, and Tatiana Tom- masi, editors,Computer Vision – ECCV 2024 Workshops, pages 377– 390, Cham, 2025. Springer Nature Switzerland

    work page 2024

  63. [63]

    Are SNNs Really More Energy-Efficient Than ANNs? 34 an In-Depth Hardware-Aware Study.IEEE Transactions on Emerging Topics in Computational Intelligence, 7(3):731–741, June 2023

    Manon Dampfhoffer, Thomas Mesquida, Alexandre Valentian, and Lorena Anghel. Are SNNs Really More Energy-Efficient Than ANNs? 34 an In-Depth Hardware-Aware Study.IEEE Transactions on Emerging Topics in Computational Intelligence, 7(3):731–741, June 2023

    work page 2023

  64. [64]

    An analytical es- timation of spiking neural networks energy efficiency

    Edgar Lemaire, Lo¨ ıc Cordone, Andrea Castagnetti, Pierre-Emmanuel Novac, Jonathan Courtois, and Benoˆ ıt Miramond. An analytical es- timation of spiking neural networks energy efficiency. In Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, and Adam Jatowt, editors,Neural Information Processing, pages 574–587, Cham, 2023. Springer Internationa...

    work page 2023

  65. [65]

    Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B

    Norman P. Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B. Jablin, George Kurian, James Laudon, Sheng Li, Peter Ma, Xiaoyu Ma, Thomas Norrie, Nishant Patil, Sushma Prasad, Cliff Young, Zongwei Zhou, and David Patterson. Ten lessons from three generations shaped google’s tpuv4i : Industrial product. In2021 ACM/IEEE 48th Annual Internationa...

    work page 2021