CSAP computes attention at the deepest scale and propagates the maps to shallower stages, bypassing per-scale query-key computations to cut decoder FLOPs while preserving multi-scale performance and beating SegNeXt-Tiny on ADE20K, Cityscapes, and COCO-Stuff.
arXiv preprint arXiv:2404.16573 (2024)
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DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.
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Cross-Stage Attention Propagation for Efficient Semantic Segmentation
CSAP computes attention at the deepest scale and propagates the maps to shallower stages, bypassing per-scale query-key computations to cut decoder FLOPs while preserving multi-scale performance and beating SegNeXt-Tiny on ADE20K, Cityscapes, and COCO-Stuff.
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Diffusion Model as a Generalist Segmentation Learner
DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.