Abstract:Entity re-identification is the foundation of tracking-and matching-based computer vision tasks, which are widely employed in a variety of applications. However, when trained exclusively on clear images, the models capacity to generalize is significantly affected by the presence of occlusion at referencing time, whereas data argumentation-based approaches are costly to construct without guaranteeing a testtime improvement. To tackle this problem, we propose a domain adaptation framework based on learning repre… Show more
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