mlsanityData to 14 Oct 2026

Bina-Rep Event Frames: a Simple and Effective Representation for Event-based cameras

arXiv 2202.13662 ↗First posted 28 Feb 2022cs.CVcited by 12

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Abstract (arXiv)

This paper presents "Bina-Rep", a simple representation method that converts asynchronous streams of events from event cameras to a sequence of sparse and expressive event frames. By representing multiple binary event images as a single frame of N-bit numbers, our method is able to obtain sparser and more expressive event frames thanks to the retained information about event orders in the original stream. Coupled with our proposed model based on a convolutional neural network, the reported results achieve state-of-the-art performance and repeatedly outperforms other common event representation methods. Our approach also shows competitive robustness against common image corruptions, compared to other representation techniques.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 28 Feb 2022.

Builds on

12 of 19 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  4. 2019Event-based Vision: A Surveycited by 1,016
  5. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  6. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  7. 2018Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotioncited by 307
  8. 2018HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classificationcited by 218
  9. 2018Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Carscited by 200
  10. 2021N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Camerascited by 92
  11. 2018Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domaincited by 15
  12. 2021Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 6; 2025: 2; 2026: 1.

12 papers.

  1. 2022Recurrent Vision Transformers for Object Detection with Event Camerascited by 159
  2. 2024Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Modelscited by 31
  3. 2022HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalitiescited by 25
  4. 2024Event-Based Eye Tracking. AIS 2024 Challenge Surveycited by 19
  5. 2023Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Camerascited by 10
  6. 2024MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye trackingcited by 8
  7. 2024EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Visioncited by 8
  8. 2025Exploring Temporal Dynamics in Event-based Eye Trackercited by 6
  9. 2024Scalable Event Cloud Network for Event-based Classificationcited by 3
  10. 2025Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Spacecited by 3
  11. 2024Low-Latency Scalable Streaming for Event-Based Visioncited by 1
  12. 2026Enhancing Eye Feature Estimation from Event Data Streams through Adaptive Inference State Space Modelingcited by 0