Summary of Efficient Event-based Object Detection: a Hybrid Neural Network with Spatial and Temporal Attention, by Soikat Hasan Ahmed et al.
Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attention
by Soikat Hasan Ahmed, Jan Finkbeiner, Emre Neftci
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel attention-based hybrid Spiking Neural Network (SNN) and Artificial Neural Network (ANN) architecture is introduced for event-based object detection, combining the strengths of both SNN and ANN. The proposed Attention-based Hybrid SNN-ANN backbone leverages sparse spatial and temporal relations from the SNN layer and converts them into dense feature maps for the ANN part. This multi-timescale network combines fast SNN processing for short timesteps with long-term dense RNN processing, effectively capturing both fast and slow dynamics. Experimental results demonstrate that this hybrid approach surpasses SNN-based approaches by significant margins, with results comparable to existing ANN and RNN-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Event cameras offer high temporal resolution and dynamic range with minimal motion blur, making them promising for robust object detection. A new attention-based hybrid Spiking Neural Network (SNN) and Artificial Neural Network (ANN) architecture is developed for event-based object detection. This approach combines the strengths of both SNN and ANN to capture fast and slow dynamics. The results show that this method is more accurate than SNN-only approaches and comparable to existing methods. |
Keywords
» Artificial intelligence » Attention » Neural network » Object detection » Rnn
