Securing a sufficient amount of paired data is important to train an
image-text retrieval (ITR) model, but collecting paired data is very expensive.
To address this issue, in this paper, we propose an active learning algorithm
for ITR that can collect paired data cost-efficiently. Previous studies assume
that image-text pairs are given and their category labels are asked to the
annotator. However, in the recent ITR studies, the importance of category label
is decreased since a retrieval model can be trained with only image-text pairs.
For this reason, we set up an active learning scenario where unpaired images
(or texts) are given and the annotator provides corresponding texts (or images)
to make paired data. The key idea of the proposed AL algorithm is to select
unpaired images (or texts) that can be hard negative samples for existing texts
(or images). To this end, we introduce a novel scoring function to choose hard
negative samples. We validate the effectiveness of the proposed method on
Flickr30K and MS-COCO datasets.

我们提出了一种主动学习算法，用于图像 - 文本检索 (ITR)，以更具成本效益地收集配对数据。通过引入一种新的评分函数来选择难以配对的图像（或文本）作为负样本，我们验证了该方法在 Flickr30K 和 MS-COCO 数据集上的有效性。