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ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification — arXiv:2608.27150

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27150
License (as archived): CC0-1.0
Archived: 2026-10-17T14:30:37.076Z
SHA-256 of the archived text: 4b4b02675a5e4ad7f62c2e4908dfd489ce57a9cbe7bf2626561ad17bdb4e0a36
Stable URL of this page: https://metraipolis.com/documento/dc_c3148c00a684

How to cite

ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification — arXiv:2608.27150 — arXiv (en), https://arxiv.org/abs/2608.27150 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c3148c00a684 (Metraipolis archive dc_c3148c00a684, sha256 4b4b02675a5e4ad7f62c2e4908dfd489ce57a9cbe7bf2626561ad17bdb4e0a36)

BibTeX

@misc{dcc3148c00a684,
  title = {ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification — arXiv:2608.27150},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27150},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c3148c00a684; sha256 4b4b02675a5e4ad7f62c2e4908dfd489ce57a9cbe7bf2626561ad17bdb4e0a36},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27150. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Object classification in event-based computer vision is a task that is attracting considerable research attention. Event-based object classification is a fundamental task in the fields of security and applied computer vision, which typically use synchronous frame-based cameras and computing pipelines for operation. This approach has several practical flaws. The size, weight and power consumption of the device could prohibit deployment at the extreme edge or in covert sensing environments. Besides this, there are security concerns inherent in cloud-based or other off-device computation approaches due to the requirement of sending and receiving potentially sensitive data. Furthermore, this transmission of data introduces latency and requires consistent connectivity to the cloud infrastructure to function. The use of Spiking Neural Networks (SNNs) hosted on neuromorphic devices attempts to solve several issues present in this conventional approach. Research into event-based object classification methods are hindered by the lack of high-quality vision datasets to use. To this end, the ANTShapes simulation tool has been previously proposed to create and label event-based vision datasets. In this paper, four novel datasets of varying difficulties are created using the tool and are benchmarked against existing spiking datasets commonly used for event-based vision research (N-MNIST, CIFAR10-DVS, DVSGesture and POKER-DVS). Classification is performed using a convolutional SNN. This work simultaneously provides four datasets with rich details for future experiments to use and validates the output of the ANTShapes dataset simulation tool as being suitable for its purpose.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-28 in cs.AI).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.27150 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T13:16:53.694Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).