mlsanityData to 7 Oct 2026

Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection

arXiv 2402.01287 ↗First posted 2 Feb 2024cs.CV cs.LG cs.NEcited by 8

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Abstract (arXiv)

In the era of AI at the edge, self-driving cars, and climate change, the need for energy-efficient, small, embedded AI is growing. Spiking Neural Networks (SNNs) are a promising approach to address this challenge, with their event-driven information flow and sparse activations. We propose Spiking CenterNet for object detection on event data. It combines an SNN CenterNet adaptation with an efficient M2U-Net-based decoder. Our model significantly outperforms comparable previous work on Prophesee's challenging GEN1 Automotive Detection Dataset while using less than half the energy. Distilling the knowledge of a non-spiking teacher into our SNN further increases performance. To the best of our knowledge, our work is the first approach that takes advantage of knowledge distillation in the field of spiking object detection.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

19 of 33 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2019Objects as Pointscited by 1,172
  5. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  6. 2019Event-based Vision: A Surveycited by 1,010
  7. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  8. 2017Soft-NMS -- Improving Object Detection With One Line of Codecited by 380
  9. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  10. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  11. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 174
  12. 2020A Large Scale Event-based Detection Dataset for Automotivecited by 108
7 more
  1. 2022Object Detection with Spiking Neural Networks on Automotive Event Datacited by 79
  2. 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 76
  3. 2023Hierarchical Neural Memory Network for Low Latency Event Processingcited by 45
  4. 2022An Analytical Estimation of Spiking Neural Networks Energy Efficiencycited by 38
  5. 2023Training Full Spike Neural Networks via Auxiliary Accumulation Pathwaycited by 26
  6. 2023Spiking Neural Network for Ultra-low-latency and High-accurate Object Detectioncited by 14
  7. 2023Biologically inspired structure learning with reverse knowledge distillation for spiking neural networkscited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 5.

8 papers.

  1. 2024EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networkscited by 16
  2. 2025Adaptive Event Stream Slicing for Open-Vocabulary Event-Based Object Detection via Vision-Language Knowledge Distillationcited by 8
  3. 2025Hybrid Spiking Vision Transformer for Object Detection with Event Camerascited by 7
  4. 2026BiSpikCLM: A Spiking Language Model integrating Softmax-Free Spiking Attention and Spike-Aware Alignment Distillationcited by 1
  5. 2026Efficient Multi-Timescale Event Representations for Feed-Forward Object Detectioncited by 1
  6. 2026CMTFormer: Marrying Transformer with Hierarchical Information Interaction for RGB-Event Object Detectioncited by 0
  7. 2026Neuromorphic Object Detection: An In-Depth Study and Future Directionscited by 0
  8. 2026Sequence-SOD: Bio-inspired Sequence-aware Spiking ObjectDetection for Event Camerascited by 0