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SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation.

Guoan Xu, Jiaming Chen, Wenfeng Huang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 6, 2026

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This study introduces Strip Cross-Attention (SCASeg), a novel decoder for efficient semantic segmentation using Vision Transformers (ViT). SCASeg enhances feature interaction and speeds up inference, outperforming existing methods on benchmark datasets.

Area of Science:

Computer Vision
Deep Learning
Image Segmentation

Background:

Vision Transformers (ViT) are successful general-purpose visual encoders.
ViT backbones require specialized decoders for tasks like semantic segmentation.
Existing decoders may not fully leverage ViT capabilities for segmentation.

Purpose of the Study:

To design an efficient and effective decoder head for semantic segmentation using ViT.
To improve feature interaction and computational efficiency in ViT-based segmentation models.
To introduce a novel architecture, Strip Cross-Attention (SCASeg), for semantic segmentation.

Main Methods:

Proposed Strip Cross-Attention (SCASeg) decoder head.
Utilized lateral connections with encoder features as Queries.

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Introduced Cross-Layer Block (CLB) for unified Keys and Values representation.

Incorporated convolution for local context and compressed channels for efficiency.

Main Results:

SCASeg demonstrates competitive performance across various setups.
Outperformed leading segmentation architectures on ADE20K, Cityscapes, COCO-Stuff 164k, and Pascal VOC2012.
Achieved improved computational efficiency, reduced memory usage, and increased inference speed.

Conclusions:

SCASeg is an adaptable and efficient decoder for semantic segmentation.
The proposed methods effectively capture global and local context dependencies.
SCASeg offers a promising alternative to conventional decoders for ViT-based segmentation.