EventMix: An Efficient Augmentation Strategy for Event-Based Data

• URL: http://arxiv.org/abs/2205.12054v1
• Date: Tue, 24 May 2022 13:07:33 GMT
• Title: EventMix: An Efficient Augmentation Strategy for Event-Based Data
• Authors: Guobin Shen, Dongcheng Zhao, Yi Zeng
• Abstract summary: Event cameras can provide high dynamic range and low-energy event stream data. The scale is smaller and more difficult to obtain than traditional frame-based data. This paper proposes an efficient data augmentation strategy for event stream data: EventMix.
• Score: 4.8416725611508244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality and challenging event stream datasets play an important role in the design of an efficient event-driven mechanism that mimics the brain. Although event cameras can provide high dynamic range and low-energy event stream data, the scale is smaller and more difficult to obtain than traditional frame-based data, which restricts the development of neuromorphic computing. Data augmentation can improve the quantity and quality of the original data by processing more representations from the original data. This paper proposes an efficient data augmentation strategy for event stream data: EventMix. We carefully design the mixing of different event streams by Gaussian Mixture Model to generate random 3D masks and achieve arbitrary shape mixing of event streams in the spatio-temporal dimension. By computing the relative distances of event streams, we propose a more reasonable way to assign labels to the mixed samples. The experimental results on multiple neuromorphic datasets have shown that our strategy can improve its performance on neuromorphic datasets both for ANNs and SNNs, and we have achieved state-of-the-art performance on DVS-CIFAR10, N-Caltech101, N-CARS, and DVS-Gesture datasets.

Related papers

• EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision [9.447299017563841]
  Dynamic Vision Sensors (DVS) capture event data with high temporal resolution and low power consumption. Event data augmentation serve as an essential method for overcoming the limitation of scale and diversity in event datasets.
  arXiv  Detail & Related papers  (2024-05-29T08:39:31Z)
• Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models [2.551844666707809]
  Event-based sensors are well suited for real-time processing. Current methods either collapse events into frames or cannot scale up when processing the event data directly event-by-event.
  arXiv  Detail & Related papers  (2024-04-29T08:50:27Z)
• Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network [4.386534439007928]
  We propose a novel event-based motion segmentation algorithm using a Graph Transformer Neural Network, dubbed GTNN. Our proposed algorithm processes event streams as 3D graphs by a series nonlinear transformations to unveil local and global correlations between events. We show that GTNN outperforms state-of-the-art methods in the presence of dynamic background variations, motion patterns, and multiple dynamic objects with varying sizes and velocities.
  arXiv  Detail & Related papers  (2024-04-16T22:44:29Z)
• An Event-Oriented Diffusion-Refinement Method for Sparse Events Completion [36.64856578682197]
  Event cameras or dynamic vision sensors (DVS) record asynchronous response to brightness changes instead of conventional intensity frames. We propose an inventive event completion sequence approach conforming to unique characteristics of event data in both the processing stage and the output form. Specifically, we treat event streams as 3D event clouds in thetemporal domain, develop a diffusion-based generative model to generate dense clouds in a coarse-to-fine manner, and recover exact timestamps to maintain the temporal resolution of raw data successfully.
  arXiv  Detail & Related papers  (2024-01-06T08:09:54Z)
• Implicit Event-RGBD Neural SLAM [54.74363487009845]
  Implicit neural SLAM has achieved remarkable progress recently. Existing methods face significant challenges in non-ideal scenarios. We propose EN-SLAM, the first event-RGBD implicit neural SLAM framework.
  arXiv  Detail & Related papers  (2023-11-18T08:48:58Z)
• EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields [80.94515892378053]
  EvDNeRF is a pipeline for generating event data and training an event-based dynamic NeRF. NeRFs offer geometric-based learnable rendering, but prior work with events has only considered reconstruction of static scenes. We show that by training on varied batch sizes of events, we can improve test-time predictions of events at fine time resolutions.
  arXiv  Detail & Related papers  (2023-10-03T21:08:41Z)
• Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation [79.02808071245634]
  Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Event streams are divided into grids in the x-y-t coordinates for both positive and negative polarity, producing a set of pillars as 3D tensor representation. Long memory is encoded in the hidden state of adaptive convLSTMs while short memory is modeled by computing spatial-temporal correlation between event pillars.
  arXiv  Detail & Related papers  (2023-03-17T12:12:41Z)
• Event Data Association via Robust Model Fitting for Event-based Object Tracking [66.05728523166755]
  We propose a novel Event Data Association (called EDA) approach to explicitly address the event association and fusion problem. The proposed EDA seeks for event trajectories that best fit the event data, in order to perform unifying data association and information fusion. The experimental results show the effectiveness of EDA under challenging scenarios, such as high speed, motion blur, and high dynamic range conditions.
  arXiv  Detail & Related papers  (2021-10-25T13:56:00Z)
• Robust Event Classification Using Imperfect Real-world PMU Data [58.26737360525643]
  We study robust event classification using imperfect real-world phasor measurement unit (PMU) data. We develop a novel machine learning framework for training robust event classifiers.
  arXiv  Detail & Related papers  (2021-10-19T17:41:43Z)
• EventDrop: data augmentation for event-based learning [0.3670422696827526]
  EventDrop is a new method for augmenting asynchronous event data to improve the generalization of deep models. From a practical perspective, EventDrop is simple to implement and computationally low-cost.
  arXiv  Detail & Related papers  (2021-06-07T11:53:14Z)
• Differentiable Event Stream Simulator for Non-Rigid 3D Tracking [82.56690776283428]
  Our differentiable simulator enables non-rigid 3D tracking of deformable objects from event streams. We show the effectiveness of our approach for various types of non-rigid objects and compare to existing methods for non-rigid 3D tracking.
  arXiv  Detail & Related papers  (2021-04-30T17:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.