Attention-based Adversarial Appearance Learning of Augmented Pedestrians
Kevin Strauss,
Artem Savkin,
Federico Tombari
Abstract:Synthetic data became already an essential component of machine learning-based perception in the field of autonomous driving. Yet it still cannot replace real data completely due to the sim2real domain shift. In this work, we propose a method that leverages the advantages of the augmentation process and adversarial training to synthesize realistic data for the pedestrian recognition task. Our approach utilizes an attention mechanism driven by an adversarial loss to learn domain discrepancies and improve sim2re… Show more
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