Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network

• URL: http://arxiv.org/abs/2304.11857v2
• Date: Sun, 9 Jul 2023 08:30:41 GMT
• Title: Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network
• Authors: Rui Zhang, Luziwei Leng, Kaiwei Che, Hu Zhang, Jie Cheng, Qinghai Guo, Jiangxing Liao and Ran Cheng
• Abstract summary: We present an efficient spiking encoder-decoder network designed for large-scale event-based semantic segmentation tasks. To enhance learning from dynamic event streams, we harness the inherent adaptive threshold of spiking neurons to modulate network activation. Our proposed network achieves a 72.57% mean intersection over union (MIoU) on the DDD17 dataset and a 57.22% MIoU on the recently introduced, larger DSEC-Semantic dataset.
• Score: 10.77500756739271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging the low-power, event-driven computation and the inherent temporal dynamics, spiking neural networks (SNNs) are potentially ideal solutions for processing dynamic and asynchronous signals from event-based sensors. However, due to the challenges in training and the restrictions in architectural design, there are limited examples of competitive SNNs in the realm of event-based dense prediction when compared to artificial neural networks (ANNs). In this paper, we present an efficient spiking encoder-decoder network designed for large-scale event-based semantic segmentation tasks. This is achieved by optimizing the encoder using a hierarchical search method. To enhance learning from dynamic event streams, we harness the inherent adaptive threshold of spiking neurons to modulate network activation. Moreover, we introduce a dual-path Spiking Spatially-Adaptive Modulation (SSAM) block, specifically designed to enhance the representation of sparse events, thereby considerably improving network performance. Our proposed network achieves a 72.57% mean intersection over union (MIoU) on the DDD17 dataset and a 57.22% MIoU on the recently introduced, larger DSEC-Semantic dataset. This performance surpasses the current state-of-the-art ANNs by 4%, whilst consuming significantly less computational resources. To the best of our knowledge, this is the first study demonstrating SNNs outperforming ANNs in demanding event-based semantic segmentation tasks, thereby establishing the vast potential of SNNs in the field of event-based vision. Our source code will be made publicly accessible.

Related papers

• EvSegSNN: Neuromorphic Semantic Segmentation for Event Data [0.6138671548064356]
  EvSegSNN is a biologically plausible encoder-decoder U-shaped architecture relying on Parametric Leaky Integrate and Fire neurons. We introduce an end-to-end biologically inspired semantic segmentation approach by combining Spiking Neural Networks with event cameras. Experiments conducted on DDD17 demonstrate that EvSegSNN outperforms the closest state-of-the-art model in terms of MIoU.
  arXiv  Detail & Related papers  (2024-06-20T10:36:24Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Object Detection with Spiking Neural Networks on Automotive Event Data [0.0]
  We propose to train spiking neural networks (SNNs) directly on data coming from event cameras to design fast and efficient automotive embedded applications. In this paper, we conducted experiments on two automotive event datasets, establishing new state-of-the-art classification results for spiking neural networks.
  arXiv  Detail & Related papers  (2022-05-09T14:39:47Z)
• 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)
• Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation [5.800785186389827]
  Spiking Neural Networks (SNNs) have emerged as the low-power alternative to Artificial Neural Networks (ANNs) In this paper, we explore the SNN applications beyond classification and present semantic segmentation networks configured with spiking neurons.
  arXiv  Detail & Related papers  (2021-10-14T21:53:03Z)
• Multi-Exit Semantic Segmentation Networks [78.44441236864057]
  We propose a framework for converting state-of-the-art segmentation models to MESS networks. specially trained CNNs that employ parametrised early exits along their depth to save during inference on easier samples. We co-optimise the number, placement and architecture of the attached segmentation heads, along with the exit policy, to adapt to the device capabilities and application-specific requirements.
  arXiv  Detail & Related papers  (2021-06-07T11:37:03Z)
• SpikeMS: Deep Spiking Neural Network for Motion Segmentation [7.491944503744111]
  textitSpikeMS is the first deep encoder-decoder SNN architecture for the real-world large-scale problem of motion segmentation. We show that textitSpikeMS is capable of textitincremental predictions, or predictions from smaller amounts of test data than it is trained on.
  arXiv  Detail & Related papers  (2021-05-13T21:34:55Z)
• Binary Graph Neural Networks [69.51765073772226]
  Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
  arXiv  Detail & Related papers  (2020-12-31T18:48:58Z)
• 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)

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.