Papers โ€บ SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention...

SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and ๐’ช(T) Complexity

15 May 2025arXiv:2505.10352archive 2025-07-28

Shihao Zou, Qingfeng Li, Wei Ji, Jingjing Li, Yongkui Yang, Guoqi Li, Chao Dong

Spiking Neural Networks (SNNs) have shown competitive performance to Artificial Neural Networks (ANNs) in various vision tasks, while offering superior energy efficiency. However, existing SNN-based Transformers primarily focus on single-image tasks, emphasizing spatial features while not effectively leveraging SNNs' efficiency in video-based vision tasks. In this paper, we introduce SpikeVideoFormer, an efficient spike-driven video Transformer, featuring linear temporal complexity ๐’ช(T). Specifically, we design a spike-driven Hamming attention (SDHA) which provides a theoretically guided adaptation from traditional real-valued attention to spike-driven attention. Building on SDHA, we further analyze various spike-driven space-time attention designs and identify an optimal scheme that delivers appealing performance for video tasks, while maintaining only linear temporal complexity. The generalization ability and efficiency of our model are demonstrated across diverse downstream video tasks, including classification, human pose tracking, and semantic segmentation. Empirical results show our method achieves state-of-the-art (SOTA) performance compared to existing SNN approaches, with over 15\% improvement on the latter two tasks. Additionally, it matches the performance of recent ANN-based methods while offering significant efficiency gains, achieving ร—16, ร—10 and ร—5 improvements on the three tasks. https://github.com/JimmyZou/SpikeVideoFormer

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build_dataset jimmyzou/spikevideoformer/classification/dataloader/datasets.py official repository unverified Apache-2.0 (permissive) ยท 9bcbccb90a4be753 ยท report
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Pose TrackingSemantic Segmentation

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Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutFocusLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSNNSoftmaxTransformer

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