ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation

• URL: http://arxiv.org/abs/2503.10195v2
• Date: Sun, 27 Apr 2025 04:02:42 GMT
• Title: ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation
• Authors: Hongze Sun, Jun Wang, Wuque Cai, Duo Chen, Qianqian Liao, Jiayi He, Yan Cui, Dezhong Yao, Daqing Guo,
• Abstract summary: Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation.<n>In this work, we propose a novel neural network architecture, ST-FlowNet, specifically tailored for optical flow estimation from event-based data.
• Score: 8.521151565928621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their ability to leverage spatio-temporal information and low-power capabilities. However, the performance of SNN models is often constrained, limiting their application in real-world scenarios. In this work, we address this gap by proposing a novel neural network architecture, ST-FlowNet, specifically tailored for optical flow estimation from event-based data. The ST-FlowNet architecture integrates ConvGRU modules to facilitate cross-modal feature augmentation and temporal alignment of the predicted optical flow, improving the network's ability to capture complex motion dynamics. Additionally, to overcome the challenges associated with training SNNs, we introduce a novel approach to derive SNN models from pre-trained artificial neural networks (ANNs) through ANN-to-SNN conversion or our proposed BISNN method. Notably, the BISNN method alleviates the complexities involved in biological parameter selection, further enhancing the robustness of SNNs in optical flow estimation tasks. Extensive evaluations on three benchmark event-based datasets demonstrate that the SNN-based ST-FlowNet model outperforms state-of-the-art methods, delivering superior performance in accurate optical flow estimation across a diverse range of dynamic visual scenes. Furthermore, the inherent energy efficiency of SNN models is highlighted, establishing a compelling advantage for their practical deployment. Overall, our work presents a novel framework for optical flow estimation using SNNs and event-based data, contributing to the advancement of neuromorphic vision applications.

Related papers

• SpikeAtConv: An Integrated Spiking-Convolutional Attention Architecture for Energy-Efficient Neuromorphic Vision Processing [11.687193535939798]
  Spiking Neural Networks (SNNs) offer a biologically inspired alternative to conventional artificial neural networks. SNNs have yet to achieve competitive performance on complex visual tasks, such as image classification. This study introduces a novel SNN architecture designed to enhance efficacy and task accuracy.
  arXiv  Detail & Related papers  (2024-11-26T13:57:38Z)
• Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods [33.377770671553336]
  Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs) In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance.
  arXiv  Detail & Related papers  (2024-05-06T09:58:54Z)
• Efficient and Effective Time-Series Forecasting with Spiking Neural Networks [47.371024581669516]
  Spiking neural networks (SNNs) provide a unique pathway for capturing the intricacies of temporal data. Applying SNNs to time-series forecasting is challenging due to difficulties in effective temporal alignment, complexities in encoding processes, and the absence of standardized guidelines for model selection. We propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information.
  arXiv  Detail & Related papers  (2024-02-02T16:23:50Z)
• Fully Spiking Denoising Diffusion Implicit Models [61.32076130121347]
  Spiking neural networks (SNNs) have garnered considerable attention owing to their ability to run on neuromorphic devices with super-high speeds. We propose a novel approach fully spiking denoising diffusion implicit model (FSDDIM) to construct a diffusion model within SNNs. We demonstrate that the proposed method outperforms the state-of-the-art fully spiking generative model.
  arXiv  Detail & Related papers  (2023-12-04T09:07:09Z)
• Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics [6.309365332210523]
  Spiking Neural Networks (SNNs) with their neuro-inspired event-driven processing can efficiently handle asynchronous data. We propose an adaptive fully-spiking framework with learnable neuronal dynamics to alleviate the spike vanishing problem. Our experiments on datasets show an average reduction of 13% in average endpoint error (AEE) compared to state-of-the-art ANNs.
  arXiv  Detail & Related papers  (2022-09-21T21:17:56Z)
• Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks [64.23331120621118]
  We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs) INNs are a class of implicit learning models that use implicit equations as layers. We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
  arXiv  Detail & Related papers  (2022-04-01T03:31:27Z)
• Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision [64.71260357476602]
  Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames. Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks. We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
  arXiv  Detail & Related papers  (2021-12-06T23:45:58Z)
• Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks [3.7384509727711923]
  A major challenge for neuromorphic computing is that learning algorithms for traditional artificial neural networks (ANNs) do not transfer directly to spiking neural networks (SNNs) In this article, we focus on the self-supervised learning problem of optical flow estimation from event-based camera inputs. We show that the performance of the proposed ANNs and SNNs are on par with that of the current state-of-the-art ANNs trained in a self-supervised manner.
  arXiv  Detail & Related papers  (2021-06-03T14:03:41Z)
• Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks [54.27962244835622]
  This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs) In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
  arXiv  Detail & Related papers  (2020-07-03T01:37:16Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)
• SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking [20.595208488431766]
  SiamSNN is the first deep SNN tracker that achieves short latency and low precision loss on the visual object tracking benchmarks OTB2013, VOT2016, and GOT-10k. SiamSNN notably achieves low energy consumption and real-time on Neuromorphic chip TrueNorth.
  arXiv  Detail & Related papers  (2020-03-17T08:49:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.