SinkSAM-Net uses topographic priors and SAM with coordinate-wise bounding box jittering to create pseudo-labels for iterative self-supervised training of an EfficientNetV2-UNet, reaching about 95% of fully supervised performance on sinkhole datasets.
Application of segment anything model for civil infrastructure defect assessment,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2024 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A survey that reviews efficient variants of the Segment Anything Model, categorizes acceleration strategies, and provides a unified hardware evaluation on benchmarks.
citing papers explorer
-
SinkSAM-Net: Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Model
SinkSAM-Net uses topographic priors and SAM with coordinate-wise bounding box jittering to create pseudo-labels for iterative self-supervised training of an EfficientNetV2-UNet, reaching about 95% of fully supervised performance on sinkhole datasets.
-
On Efficient Variants of Segment Anything Model: A Survey
A survey that reviews efficient variants of the Segment Anything Model, categorizes acceleration strategies, and provides a unified hardware evaluation on benchmarks.