ArticleFrontiers in neuroscience2018
Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Direct training high-performance deep spiking neural networks: a review of theories and methods.Frontiers in neuroscience · 2024Review
- Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks.Scientific reports · 2023Article
- Heterogeneous recurrent spiking neural network for spatio-temporal classification.Frontiers in neuroscience · 2023Article
- Model-size reduction for reservoir computing by concatenating internal states through time.Scientific reports · 2020Article
- On Improving The Computing Capacity of Dynamical Systems.Scientific reports · 2020Article
- Reinforcement Learning With Low-Complexity Liquid State Machines.Frontiers in neuroscience · 2019Article
- Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines.Frontiers in neuroscience · 2019Article
- SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition.Frontiers in neuroscience · 2018Article
- Deep Learning With Spiking Neurons: Opportunities and Challenges.Frontiers in neuroscience · 2018Review
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Authors and funding
2 authors.
Funding
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Abstract
A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competitive accuracy with respect to state-of-the-art non-spiking neural models.
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