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Models/DGCNN + SageMix

DGCNN + SageMix

Reported on 8 benchmarks across 5 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

Shape Representation Of 3D Point CloudsonScanObjectNN
Overall Accuracy· 2022-10-13
83.6
best: 97.2 (OmniVec2)
SageMix: Saliency-Guided Mixup for Point Clouds arXiv:2210.06944
Shape Representation Of 3D Point CloudsonModelNet40
Overall Accuracy· 2022-10-13
93.6
best: 95.3 (PointGST)
SageMix: Saliency-Guided Mixup for Point Clouds arXiv:2210.06944
3D Point Cloud ClassificationonScanObjectNN
Overall Accuracy· 2022-10-13
83.6
best: 97.2 (OmniVec2)
SageMix: Saliency-Guided Mixup for Point Clouds arXiv:2210.06944
3D Point Cloud ClassificationonModelNet40
Overall Accuracy· 2022-10-13
93.6
best: 95.3 (PointGST)
SageMix: Saliency-Guided Mixup for Point Clouds arXiv:2210.06944
3D Point Cloud ReconstructiononScanObjectNN
Overall Accuracy· 2022-10-13
83.6
best: 97.2 (OmniVec2)
SageMix: Saliency-Guided Mixup for Point Clouds arXiv:2210.06944
3D Point Cloud ReconstructiononModelNet40
Overall Accuracy· 2022-10-13
93.6
best: 95.3 (PointGST)
SageMix: Saliency-Guided Mixup for Point Clouds arXiv:2210.06944

Medical1 result

Semantic SegmentationonShapeNet-Part
Instance Average IoU· 2022-10-13
85.4
best: 89.1 (GeomGCNN)
SageMix: Saliency-Guided Mixup for Point Clouds arXiv:2210.06944

Audio1 result

10-shot image generationonShapeNet-Part
Instance Average IoU· 2022-10-13
85.4
best: 89.1 (GeomGCNN)
SageMix: Saliency-Guided Mixup for Point Clouds arXiv:2210.06944