SegSTRONG-C provides a new benchmark where top models reach 0.9394 DSC and 0.9301 NSD on corrupted surgical tool segmentation tests, showing conventional techniques help but calling for more innovative robustness methods.
In: Proceedings of the European Conference on Computer Vision 30 (ECCV), pp
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MS-SSE-Net integrates multi-scale feature extraction and squeeze-and-excitation attention into DenseNet201, reaching 99.26% accuracy on the StructDamage dataset and outperforming the baseline by about 0.73 percentage points.
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SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge
SegSTRONG-C provides a new benchmark where top models reach 0.9394 DSC and 0.9301 NSD on corrupted surgical tool segmentation tests, showing conventional techniques help but calling for more innovative robustness methods.
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MS-SSE-Net: A Multi-Scale Spatial Squeeze-and-Excitation Network for Structural Damage Detection in Civil and Geotechnical Engineering
MS-SSE-Net integrates multi-scale feature extraction and squeeze-and-excitation attention into DenseNet201, reaching 99.26% accuracy on the StructDamage dataset and outperforming the baseline by about 0.73 percentage points.