Summary of Sdformerflow: Spatiotemporal Swin Spikeformer For Event-based Optical Flow Estimation, by Yi Tian and Juan Andrade-cetto
SDformerFlow: Spatiotemporal swin spikeformer for event-based optical flow estimation
by Yi Tian, Juan Andrade-Cetto
First submitted to arxiv on: 6 Sep 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 The proposed solutions, STTFlowNet and SDformerFlow, leverage spatiotemporal shifted window self-attention (swin) transformer encoders for fast and robust optical flow estimation from event cameras. Inspired by the success of transformers in computer vision tasks, these SNN-based approaches share similar asynchronous and sparse characteristics with event cameras, making them well-suited for processing this type of data. The architectures incorporate fully connected layers, convolutional layers, and spikeformer encoders, which process spike trains to estimate optical flow. Our work presents the first use of spikeformers for dense optical flow estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Event cameras capture changes in light intensity, offering a higher dynamic range and faster data rate than conventional frame-based cameras. Spiking neural networks (SNNs) can process this data efficiently, making them suitable for scenarios with fast motion or challenging lighting conditions. This paper proposes two solutions for optical flow estimation: STTFlowNet and SDformerFlow. These SNN-based approaches use transformer encoders to estimate optical flow from event camera data. The results show state-of-the-art performance on the DSEC and MVSEC datasets, with significant power consumption reduction compared to equivalent ANNs. |
Keywords
» Artificial intelligence » Optical flow » Self attention » Spatiotemporal » Transformer
