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Progressively multi-scale feature fusion for semantic segmentation
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DOI：10.1016/j.jvcir.2026.104739.png)
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• We propose a progressive multi-scale feature fusion decoder that achieves precise semantic segmentation with outstanding performance. • It effectively addresses pixel-level misalignment and confusion caused by bilinear upsampling. • The decoder is highly adaptable and versatile, compatible with any backbone network. • It achieves optimal segmentation performance with minimal parameters, resulting in reduced inference time and memory footprint.
期刊
IF:
3.1
论文数:
547
被引数:
5.6K
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