Papers Event data classification
“Event data classification” 태그가 달린 논문 12편 · 필터 해제
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 ClassificationEcsnet: Spatio-temporal feature learning for event camera
The neuromorphic event cameras can efficiently sense the latent geometric structures and motion clues of a scene by generating asynchronous and sparse event signals. Due to the irregular layout of the event signals, how …
Action RecognitionEvent-based visionEvent data classificationGesture GenerationA Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks
Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the …
Event data classificationGesture RecognitionSound Event Localization and DetectionNeuromorphic Data Augmentation for Training Spiking Neural Networks
Developing neuromorphic intelligence on event-based datasets with Spiking Neural Networks (SNNs) has recently attracted much research attention. However, the limited size of event-based datasets makes SNNs prone to overf…
Contrastive LearningData AugmentationEvent data classificationDifferentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks
Spiking Neural Networks (SNNs) have emerged as a biology-inspired method mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy efficiency of inference on neuromorphic hardware. However, it …
Event data classificationImage ClassificationConvolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature. Also, they have the advantage of low computation cost in contrast to con…
Activity Recognition In VideosEvent data classificationImage ClassificationVideo ClassificationA predictive model for the identification of learning styles in MOOC environments
Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners. In parallel, recent advancements in machine learning techniques and b…
Anomaly DetectionAutomatic Machine Learning Model SelectionClustering Algorithms EvaluationEvent data classification