# PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection

arXiv: https://arxiv.org/abs/2105.09909 · HTML: https://mlsanity.com/paper/2105.09909/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 20 of 20)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Spatiotemporal Contrastive Video Representation Learning](https://mlsanity.com/paper/2008.03800/) (2020)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples](https://mlsanity.com/paper/2103.05905/) (2021)
- [Oops! Predicting Unintentional Action in Video](https://mlsanity.com/paper/1911.11206/) (2019)
- [SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning](https://mlsanity.com/paper/2008.00975/) (2020)
- [Watching the World Go By: Representation Learning from Unlabeled Videos](https://mlsanity.com/paper/2003.07990/) (2020)
- [Video Representation Learning with Visual Tempo Consistency](https://mlsanity.com/paper/2006.15489/) (2020)
- [Resource Efficient 3D Convolutional Neural Networks](https://mlsanity.com/paper/1904.02422/) (2019)
- [Towards Universal Representation for Unseen Action Recognition](https://mlsanity.com/paper/1803.08460/) (2018)
- [Towards Good Practices for Very Deep Two-Stream ConvNets](https://mlsanity.com/paper/1507.02159/) (2015)
- [Hidden Two-Stream Convolutional Networks for Action Recognition](https://mlsanity.com/paper/1704.00389/) (2017)
- [ActionFlowNet: Learning Motion Representation for Action Recognition](https://mlsanity.com/paper/1612.03052/) (2016)
- [Pose And Joint-Aware Action Recognition](https://mlsanity.com/paper/2010.08164/) (2020)
- [Reinforcement Learning with Low-Complexity Liquid State Machines](https://mlsanity.com/paper/1906.01695/) (2019)
- [HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN](https://mlsanity.com/paper/1912.04430/) (2019)
- [Convolutional Drift Networks for Video Classification](https://mlsanity.com/paper/1711.01201/) (2017)

## Built on by (most cited first, 2 of 2)

- [Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks](https://mlsanity.com/paper/2304.01015/) (2023)
- [A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits](https://mlsanity.com/paper/2609.08070/) (2026)

## Abstract (arXiv)

Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms. Liquid State Machine (LSM) is a bio-inspired RC model that mimics the cortical microcircuits and uses spiking neural networks (SNN) that can be directly realized on neuromorphic hardware. In this paper, we present a novel Parallelized LSM (PLSM) architecture that incorporates spatio-temporal read-out layer and semantic constraints on model output. To the best of our knowledge, such a formulation has been done for the first time in literature, and it offers a computationally lighter alternative to traditional deep-learning models. Additionally, we also present a comprehensive algorithm for the implementation of parallelizable SNNs and LSMs that are GPU-compatible. We implement the PLSM model to classify unintentional/accidental video clips, using the Oops dataset. From the experimental results on detecting unintentional action in video, it can be observed that our proposed model outperforms a self-supervised model and a fully supervised traditional deep learning model. All the implemented codes can be found at our repository https://github.com/anonymoussentience2020/Parallelized_LSM_for_Unintentional_Action_Recognition.