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SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision
[article]
2023
arXiv
pre-print
Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the
arXiv:2207.06242v2
fatcat:sgyxdgqcabd77mg3pkjwvzx4q4