mlsanityData to 7 Oct 2026

A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanism

arXiv 2409.12691 ↗First posted 19 Sep 2024cs.CVcited by 0

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Abstract (arXiv)

Spiking Neural Networks (SNNs) are well-suited for processing event streams from Dynamic Visual Sensors (DVSs) due to their use of sparse spike-based coding and asynchronous event-driven computation. To extract features from DVS objects, SNNs commonly use event-driven convolution with fixed kernel parameters. These filters respond strongly to features in specific orientations while disregarding others, leading to incomplete feature extraction. To improve the current event-driven convolution feature extraction capability of SNNs, we propose a DVS object recognition model that utilizes a trainable event-driven convolution and a spiking attention mechanism. The trainable event-driven convolution is proposed in this paper to update its convolution kernel through gradient descent. This method can extract local features of the event stream more efficiently than traditional event-driven convolution. Furthermore, the spiking attention mechanism is used to extract global dependence features. The classification performances of our model are better than the baseline methods on two neuromorphic datasets including MNIST-DVS and the more complex CIFAR10-DVS. Moreover, our model showed good classification ability for short event streams. It was shown that our model can improve the performance of event-driven convolutional SNNs for DVS objects.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 56 references that are in the map. First posted on arXiv 19 Sep 2024.

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31 of 56 references.

References · 31

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,096
  3. 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
  4. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  5. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  6. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  7. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 263
  8. 2019End-to-End Learning of Representations for Asynchronous Event-Based Datacited by 216
  9. 2021VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flowscited by 128
  10. 2021Temporal-wise Attention Spiking Neural Networks for Event Streams Classificationcited by 106
  11. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  12. 2023GET: Group Event Transformer for Event-Based Visioncited by 73
19 more
  1. 2020LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processingcited by 56
  2. 2023Graph-based Asynchronous Event Processing for Rapid Object Recognitioncited by 51
  3. 2015HFirst: A Temporal Approach to Object Recognitioncited by 50
  4. 2021Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimationcited by 50
  5. 2017DART: Distribution Aware Retinal Transform for Event-based Camerascited by 39
  6. 2023Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networkscited by 37
  7. 2023Membrane Potential Batch Normalization for Spiking Neural Networkscited by 35
  8. 2022Exploring Event Camera-based Odometry for Planetary Robotscited by 34
  9. 2022Attention Mechanism in Neural Networks: Where it Comes and Where it Goescited by 29
  10. 2021ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networkscited by 24
  11. 2022Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Networkcited by 19
  12. 2022Spiking Neural Networks for Frame-based and Event-based Single Object Localizationcited by 18
  13. 2019Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neuronscited by 14
  14. 2023Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioningcited by 14
  15. 2018Attention Mechanisms for Object Recognition with Event-Based Camerascited by 13
  16. 2024Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNNcited by 10
  17. 2022A Spatial-channel-temporal-fused Attention for Spiking Neural Networkscited by 7
  18. 2023An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Camerascited by 6
  19. 2024Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancementcited by 3

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