Event data classification
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Benchmarks
Most implemented
Pushing the boundaries of event subsampling in event-based video classification using CNNs
Event Trojan: Asynchronous Event-based Backdoor Attacks
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Online Training Through Time for Spiking Neural Networks
Ecsnet: Spatio-temporal feature learning for event camera
Papers
Pushing the boundaries of event subsampling in event-based video classification using CNNs
Event cameras offer low-power visual sensing capabilities ideal for edge-device applications. However, their high event rate, driven by high temporal details, can be restrictive in terms of bandwidth and computational re…
Event data classificationSensitivityVideo ClassificationEvent Trojan: Asynchronous Event-based Backdoor Attacks
As asynchronous event data is more frequently engaged in various vision tasks, the risk of backdoor attacks becomes more evident. However, research into the potential risk associated with backdoor attacks in asynchronous…
Event data classificationShrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network
Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of low-power neuromorphic computing. However, existing SNNs suffer from significant latency, utilizing 10 to 40 timesteps or more, to…
Data AugmentationEvent data classificationObject RecognitionPoint-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Sampled point and voxel methods are usually employed to downsample the dense events into sparse ones. After that, one popular way is to leverage a graph model which treats the sparse points/voxels as nodes and adopts gra…
Event data classificationGraph Representation LearningRepresentation LearningIM-Loss: Information Maximization Loss for Spiking Neural Networks
Spiking Neural Network (SNN), recognized as a type of biologically plausible architecture, has recently drawn much research attention. It transmits information by 0/1 spikes. This bio-mimetic mechanism of SNN demonstrate…
Event data classificationImage ClassificationQuantizationOnline Training Through Time for Spiking Neural Networks
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagati…
Event data classificationGesture RecognitionGPUImage Classification