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Lei Wang, Sha-sha Guo, Lian-hua Qu, Shuo Tian, Wei-xia Xu. M-LSM: An Improved Multi-Liquid State Machine for Event-Based Vision Recognition[J]. Journal of Computer Science and Technology. doi: 10.1007/s11390-021-1326-8
Citation: Lei Wang, Sha-sha Guo, Lian-hua Qu, Shuo Tian, Wei-xia Xu. M-LSM: An Improved Multi-Liquid State Machine for Event-Based Vision Recognition[J]. Journal of Computer Science and Technology. doi: 10.1007/s11390-021-1326-8

M-LSM: An Improved Multi-Liquid State Machine for Event-Based Vision Recognition

  • Event-based computation has recently gained increasing research interest for applications of vision recognition due to its intrinsic advantages on efficiency and speed. However, the existing event-based models for vision recognition are faced with several issues, such as large network complexity and expensive training cost. In this paper, we propose an improved multi-liquid state machine (M-LSM) method for high-performance vision recognition. Specifically, we introduce two methods, namely multi-State fusion and multi-liquid search, to optimize the LSM. Multi-state fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal information. We adapt network architecture search (NAS) to find the potential optimal architecture of multi-liquid LSM. We also train the LSM through an unsupervised learning rule spike-timing dependent plasticity (STDP). Our M-LSM is evaluated on two event-based datasets and demonstrates state-of-the-art recognition performance with superior advantages on network complexity and training cost.
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