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| Home > Publications database > A Hybrid SNN-ANN Network for Event-based Object Detection with Spatial and Temporal Attention > print |
| 001 | 1038039 | ||
| 005 | 20250203103305.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2403.10173 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-01088 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-01088 |
| 100 | 1 | _ | |a Ahmed, Soikat Hasan |0 P:(DE-Juel1)194363 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a A Hybrid SNN-ANN Network for Event-based Object Detection with Spatial and Temporal Attention |
| 260 | _ | _ | |c 2024 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1738243926_31341 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Event cameras offer high temporal resolution and dynamic range with minimal motion blur, making them promising for object detection tasks. While Spiking Neural Networks (SNNs) are a natural match for event-based sensory data and enable ultra-energy efficient and low latency inference on neuromorphic hardware, Artificial Neural Networks (ANNs) tend to display more stable training dynamics and faster convergence resulting in greater task performance. Hybrid SNN-ANN approaches are a promising alternative, enabling to leverage the strengths of both SNN and ANN architectures. In this work, we introduce the first Hybrid Attention-based SNN-ANN backbone for object detection using event cameras. We propose a novel Attention-based SNN-ANN bridge module to capture sparse spatial and temporal relations from the SNN layer and convert them into dense feature maps for the ANN part of the backbone. Experimental results demonstrate that our proposed method surpasses baseline hybrid and SNN-based approaches by significant margins, with results comparable to existing ANN-based methods. Extensive ablation studies confirm the effectiveness of our proposed modules and architectural choices. These results pave the way toward a hybrid SNN-ANN architecture that achieves ANN like performance at a drastically reduced parameter budget. We implemented the SNN blocks on digital neuromorphic hardware to investigate latency and power consumption and demonstrate the feasibility of our approach. |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a GREENEDGE - Taming the environmental impact of mobile networks through GREEN EDGE computing platforms (953775) |0 G:(EU-Grant)953775 |c 953775 |f H2020-MSCA-ITN-2020 |x 1 |
| 536 | _ | _ | |a BMBF 03ZU1106CA - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - A (03ZU1106CA) |0 G:(BMBF)03ZU1106CA |c 03ZU1106CA |x 2 |
| 536 | _ | _ | |a BMBF 03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - B (BMBF-03ZU1106CB) |0 G:(DE-Juel1)BMBF-03ZU1106CB |c BMBF-03ZU1106CB |x 3 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Computer Vision and Pattern Recognition (cs.CV) |2 Other |
| 650 | _ | 7 | |a Artificial Intelligence (cs.AI) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Finkbeiner, Jan |0 P:(DE-Juel1)190112 |b 1 |u fzj |
| 700 | 1 | _ | |a Neftci, Emre |0 P:(DE-Juel1)188273 |b 2 |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2403.10173 |
| 856 | 4 | _ | |u https://doi.org/10.48550/arXiv.2403.10173 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1038039/files/A%20Hybrid%20SNN-ANN%20Network%20for%20Event-based%20Object%20Detection%20with%20Spatial%20and%20Temporal%20Attention.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1038039 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)194363 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)190112 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)188273 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5234 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-15-20210701 |k PGI-15 |l Neuromorphic Software Eco System |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-15-20210701 |
| 980 | 1 | _ | |a FullTexts |
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