TERDNet introduces a transformer-encoder recurrent-decoder architecture for scene change detection that outperforms prior models on public benchmarks.
Swin transformer: Hierarchical vision transformer using shifted windows,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
LSFormer uses local structure-aware spiking self-attention and spiking response pooling to cut global attention bottlenecks, delivering 4.3% and 8.6% accuracy gains on Tiny-ImageNet and N-CALTECH101 over prior transformer-based SNNs.
SANet augments U-Net with a Dual-path Semantic-aware Module using pinwheel convolutions and CBAM, plus a Selective Attention Fusion Module for adaptive cross-scale feature fusion, to improve detection of sub-pixel infrared targets.
citing papers explorer
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TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection
TERDNet introduces a transformer-encoder recurrent-decoder architecture for scene change detection that outperforms prior models on public benchmarks.
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Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention
LSFormer uses local structure-aware spiking self-attention and spiking response pooling to cut global attention bottlenecks, delivering 4.3% and 8.6% accuracy gains on Tiny-ImageNet and N-CALTECH101 over prior transformer-based SNNs.
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Selective Attention-Based Network for Robust Infrared Small Target Detection
SANet augments U-Net with a Dual-path Semantic-aware Module using pinwheel convolutions and CBAM, plus a Selective Attention Fusion Module for adaptive cross-scale feature fusion, to improve detection of sub-pixel infrared targets.