论文标题
重新访问酒店-50k和酒店ID
Revisiting Hotels-50K and Hotel-ID
论文作者
论文摘要
在本文中，我们建议对两个最近的酒店识别数据集进行重新访问版本：酒店50k和酒店ID。重新审视的版本提供了不同级别难度的评估设置，以更好地与预期的现实应用程序（即对人口贩运进行反对）。现实世界中的场景涉及当前数据集中未捕获的酒店和位置，因此，重要的是考虑真正看不见的评估设置，这一点很重要。我们使用多个最先进的图像检索模型测试此设置，并表明，如预期的那样，随着评估越来越接近现实世界中看不见的设置，模型的性能会降低。最佳性能模型的排名也会在不同的评估设置中发生变化，这进一步促进了使用拟议的重新访问数据集。
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels50K and Hotel-ID. The revisited versions provide evaluation setups with different levels of difficulty to better align with the intended real-world application, i.e. countering human trafficking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models' performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.
