Cross-Stage Attention Propagation for Efficient Semantic Segmentation
Recent lightweight semantic segmentation methods have made significant progress by combining compact backbones with efficient decoder heads. However, most multi-scale decoders compute attention independently at each feature scale, introducing substantial redundancy since the resulting attention distributions across scales are strongly correlated. We propose Cross-Stage Attention Propagation (CSAP), a decoder framework that computes attention at the deepest feature scale and propagates the resulting attention maps to shallower stages, bypassing query-key computation at those stages entirely. This design preserves multi-scale contextual reasoning while substantially reducing the decoder's c...
| Authors | Beoungwoo Kang |
|---|---|
| Primary category | cs.CV |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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