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arXiv · 2601.11310

Context-Aware Semantic Segmentation via Stage-Wise Attention

Antoine Carreaud·Elias Naha·Arthur Chansel·Nina Lahellec·Jan Skaloud·Adrien Gressin
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Abstract

Semantic ultra-high-resolution (UHR) image segmentation is essential in remote sensing applications such as aerial mapping and environmental monitoring. Transformer-based models remain challenging in this setting because memory grows quadratically with the number of tokens, limiting either spatial resolution or contextual scope. We introduce CASWiT (Context-Aware Stage-Wise Transformer), a dual-branch Swin-based architecture that injects low-resolution contextual information into fine-grained high-resolution features through lightweight stage-wise cross-attention. To strengthen cross-scale learning, we also propose a SimMIM-style pretraining strategy based on masked reconstruction of the high-resolution image. Extensive experiments on the large-scale FLAIR-HUB aerial dataset demonstrate the effectiveness of CASWiT. Under our RGB-only UHR protocol, CASWiT reaches 66.37% mIoU with a SegFormer decoder, improving over strong RGB baselines while also improving boundary quality. On the URUR benchmark, CASWiT reaches 49.2% mIoU under the official evaluation protocol, and it also transfers effectively to medical UHR segmentation benchmarks. Code and pretrained models are available at https://huggingface.co/collections/heig-vd-geo/caswit

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Antoine Carreaud, Elias Naha, Arthur Chansel, Nina Lahellec, Jan Skaloud, Adrien Gressin. 2026-01-16. Context-Aware Semantic Segmentation via Stage-Wise Attention. https://arxiv.org/abs/2601.11310

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