# Graph-Based Object Classification for Neuromorphic Vision Sensing

arXiv: https://arxiv.org/abs/1908.06648 · HTML: https://mlsanity.com/paper/1908.06648/

## Key facts

- Cited by 85 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 30 of 85)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras](https://mlsanity.com/paper/2112.01041/) (2021)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://mlsanity.com/paper/2103.06108/) (2021)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection](https://mlsanity.com/paper/2304.13455/) (2023)
- [Graph-based Asynchronous Event Processing for Rapid Object Recognition](https://mlsanity.com/paper/2308.14419/) (2023)
- [Event Transformer. A sparse-aware solution for efficient event data processing](https://mlsanity.com/paper/2204.03355/) (2022)
- [EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation](https://mlsanity.com/paper/2111.12341/) (2021)
- [Masked Event Modeling: Self-Supervised Pretraining for Event Cameras](https://mlsanity.com/paper/2212.10368/) (2022)
- [A Voxel Graph CNN for Object Classification with Event Cameras](https://mlsanity.com/paper/2106.00216/) (2021)
- [EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding](https://mlsanity.com/paper/2308.03135/) (2023)
- [Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation](https://mlsanity.com/paper/2109.01801/) (2021)
- [Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras](https://mlsanity.com/paper/2211.12324/) (2022)
- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events](https://mlsanity.com/paper/2308.09383/) (2023)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition](https://mlsanity.com/paper/2308.04369/) (2023)
- [OmniBind: Teach to Build Unequal-Scale Modality Interaction for Omni-Bind of All](https://mlsanity.com/paper/2405.16108/) (2024)
- [UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks](https://mlsanity.com/paper/2503.12905/) (2025)
- [Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition](https://mlsanity.com/paper/2306.11546/) (2023)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [Event Transformer+. A multi-purpose solution for efficient event data processing](https://mlsanity.com/paper/2211.12222/) (2022)
- [Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and Algorithms](https://mlsanity.com/paper/2408.09764/) (2024)
- [Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras](https://mlsanity.com/paper/2307.14124/) (2023)
- [Meta-learning Spiking Neural Networks with Surrogate Gradient Descent](https://mlsanity.com/paper/2201.10777/) (2022)
- [Spiking Neural Networks for event-based action recognition: A new task to understand their advantage](https://mlsanity.com/paper/2209.14915/) (2022)
- [Towards Robust Event-based Networks for Nighttime via Unpaired Day-to-Night Event Translation](https://mlsanity.com/paper/2407.10703/) (2024)
- [A Recurrent YOLOv8-based framework for Event-Based Object Detection](https://mlsanity.com/paper/2408.05321/) (2024)

## Abstract (arXiv)

Neuromorphic vision sensing (NVS) devices represent visual information as sequences of asynchronous discrete events (a.k.a., ``spikes'') in response to changes in scene reflectance. Unlike conventional active pixel sensing (APS), NVS allows for significantly higher event sampling rates at substantially increased energy efficiency and robustness to illumination changes. However, object classification with NVS streams cannot leverage on state-of-the-art convolutional neural networks (CNNs), since NVS does not produce frame representations. To circumvent this mismatch between sensing and processing with CNNs, we propose a compact graph representation for NVS. We couple this with novel residual graph CNN architectures and show that, when trained on spatio-temporal NVS data for object classification, such residual graph CNNs preserve the spatial and temporal coherence of spike events, while requiring less computation and memory. Finally, to address the absence of large real-world NVS datasets for complex recognition tasks, we present and make available a 100k dataset of NVS recordings of the American sign language letters, acquired with an iniLabs DAVIS240c device under real-world conditions.