# Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples Source: [Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:48:16.237Z Authors: Dae Ung Jo, Kyuewang Lee, JaeHo Chung, Jin Young Choi Published: 2024-05-25T16:50:33.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()