AGVBench benchmarks 30 augmentation strategies for vein recognition and finds mixing methods improve accuracy but harm calibration and adversarial robustness.
Random erasing data augmentation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2representative citing papers
Dynamic parameterization of standard layers can replace explicit attention for linear-time global visual modeling.
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AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition
AGVBench benchmarks 30 augmentation strategies for vein recognition and finds mixing methods improve accuracy but harm calibration and adversarial robustness.
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Linear-Time Global Visual Modeling without Explicit Attention
Dynamic parameterization of standard layers can replace explicit attention for linear-time global visual modeling.