Proposes meta-learning attack with priority-aware gradient alignment for sample-wise targeted attacks on TTA that maintain label distribution consistency with no-attack baseline.
Variational continual test-time adaptation.arXiv preprint arXiv:2402.08182, 2024
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APCoTTA introduces a continual test-time adaptation method for ALS point cloud semantic segmentation using gradient-driven layer selection, entropy-based consistency loss, and random parameter interpolation, with new benchmarks showing mIoU gains of 9-14%.
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Sample-wise Targeted Adversarial Attacks on Test-time Adaptation
Proposes meta-learning attack with priority-aware gradient alignment for sample-wise targeted attacks on TTA that maintain label distribution consistency with no-attack baseline.
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APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds
APCoTTA introduces a continual test-time adaptation method for ALS point cloud semantic segmentation using gradient-driven layer selection, entropy-based consistency loss, and random parameter interpolation, with new benchmarks showing mIoU gains of 9-14%.