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AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition

arXiv 2505.11232 ↗First posted 16 May 2025cs.CVcited by 0

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Abstract (arXiv)

Event cameras, which capture brightness changes with high temporal resolution, inherently generate a significant amount of redundant and noisy data beyond essential object structures. The primary challenge in event-based object recognition lies in effectively removing this noise without losing critical spatial-temporal information. To address this, we propose an Adaptive Graph-based Noisy Data Removal framework for Event-based Object Recognition. Specifically, our approach integrates adaptive event segmentation based on normalized density analysis, a multifactorial edge-weighting mechanism, and adaptive graph-based denoising strategies. These innovations significantly enhance the integration of spatiotemporal information, effectively filtering noise while preserving critical structural features for robust recognition. Experimental evaluations on four challenging datasets demonstrate that our method achieves superior recognition accuracies of 83.77%, 76.79%, 99.30%, and 96.89%, surpassing existing graph-based methods by up to 8.79%, and improving noise reduction performance by up to 19.57%, with an additional accuracy gain of 6.26% compared to traditional Euclidean-based techniques.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 16 May 2025.

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18 of 30 references.

References · 18

  1. 2017Graph Attention Networkscited by 8,923
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  5. 2019Event-based Vision: A Surveycited by 1,016
  6. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 748
  7. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  8. 2018HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classificationcited by 218
  9. 2019End-to-End Learning of Representations for Asynchronous Event-Based Datacited by 217
  10. 2019Events-to-Video: Bringing Modern Computer Vision to Event Camerascited by 193
  11. 2020Event-based Asynchronous Sparse Convolutional Networkscited by 104
  12. 2018Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networkscited by 97
6 more
  1. 2019Graph-Based Object Classification for Neuromorphic Vision Sensingcited by 85
  2. 2018EventNet: Asynchronous Recursive Event Processingcited by 58
  3. 2020Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classificationcited by 57
  4. 2015HFirst: A Temporal Approach to Object Recognitioncited by 50
  5. 2023E-MLB: Multilevel Benchmark for Event-Based Camera Denoisingcited by 27
  6. 2020Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networkscited by 16

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