S²-FracMix creates label-preserving augmented samples via intra-image self-saliency patch mixing and fractal pattern injection, claiming SOTA results across seven benchmarks in classification, robustness, detection, and transfer tasks.
arXiv preprint arXiv:2205.15955 (2022)
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PointCaM proposes a cut-and-mix mechanism with an Unknown-Point Simulator and Estimator to improve open-set recognition on point clouds by simulating out-of-distribution data and using multi-level features.
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$S^{2}$-FracMix: Label-Preserving Self-Saliency Mixup Augmentation
S²-FracMix creates label-preserving augmented samples via intra-image self-saliency patch mixing and fractal pattern injection, claiming SOTA results across seven benchmarks in classification, robustness, detection, and transfer tasks.
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PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning
PointCaM proposes a cut-and-mix mechanism with an Unknown-Point Simulator and Estimator to improve open-set recognition on point clouds by simulating out-of-distribution data and using multi-level features.