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Domain shifts can degrade vehicle attribute classification performance more than the choice of model architecture, revealing critical vulnerabilities in real-world applications.
Achieving a remarkable F1-Score of 93.45, VeriCam redefines how we tackle zero-shot classification of unknown classes in biased real-world datasets.
Achieving a macro accuracy of 79.7% in vehicle color recognition highlights the potential of synthetic data augmentation to address severe class imbalances in surveillance contexts.
