Researchers have introduced Spiking Local Interaction (SLI) and Adaptive Complementary Fusion (ACF) to enhance Spiking Transformers. These methods address limitations in standard Spiking Self-Attention (SSA) by introducing an attention-independent pathway for direct information exchange among neighboring spiking tokens and adaptively balancing the contributions of SSA and SLI. Experiments on various datasets, including ImageNet-1K and ADE20K, show consistent improvements in image classification, event-based recognition, and semantic segmentation. AI
IMPACT These advancements could lead to more efficient and effective spiking neural networks for various computer vision tasks.
RANK_REASON The cluster contains a research paper detailing novel methods for Spiking Transformers.
Read on arXiv cs.NE (Neural & Evolutionary) →
- Adaptive Complementary Fusion
- ADE20K
- CIFAR-10
- CIFAR-100
- CIFAR10-DVS: An Event-Stream Dataset for Object Classification
- ImageNet-1K
- QKFormer
- Spiking Local Interaction
- Spiking Self-Attention
- Spiking Transformer
- Adaptive Complementary Fusion (ACF)
- Spiking Local Interaction (SLI)
- Spiking self-attention (SSA)
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