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.

本文提出了针对酒店识别领域中的 Hotels50K 和 Hotel-ID 两个数据集的更新版本，以提供不同难度等级的评估设置，以更好地适应于拟定的真实应用领域，从而在对抗人口贩卖中起到作用。通过多种最先进的图像检索模型对此设置进行测试，结果显示，正如预期，模型性能随着评估逐渐接近真实世界的未知情况而下降。最佳模型排名也会随着不同的评估设置而改变，这进一步推动使用所提出的更新版本数据集。

重访 Hotels-50K 和 Hotel-ID

Revisiting Hotels-50K and Hotel-ID

Recent research works have shown that image retrieval models are vulnerable
to adversarial attacks, where slightly modified test inputs could lead to
problematic retrieval results. In this paper, we aim to design a provably
robust image retrieval model which keeps the most important evaluation metric
Recall@1 invariant to adversarial perturbation. We propose the first 1-nearest
neighbor (NN) image retrieval algorithm, RetrievalGuard, which is provably
robust against adversarial perturbations within an $\ell_2$ ball of calculable
radius. The challenge is to design a provably robust algorithm that takes into
consideration the 1-NN search and the high-dimensional nature of the embedding
space. Algorithmically, given a base retrieval model and a query sample, we
build a smoothed retrieval model by carefully analyzing the 1-NN search
procedure in the high-dimensional embedding space. We show that the smoothed
retrieval model has bounded Lipschitz constant and thus the retrieval score is
invariant to $\ell_2$ adversarial perturbations. Experiments on image retrieval
tasks validate the robustness of our RetrievalGuard method.

本文旨在设计一个可证明鲁棒的图像检索模型，使得最重要的评估指标 Recall@1 对抗性扰动具有不变性，通过我们所提出的第一个最近邻图像检索算法 RetrievalGuard 来实现对抗性扰动的保证鲁棒性，并通过实验验证了方法的鲁棒性。