# Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach Source: [Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:55:21.281Z Authors: Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh, In So Kweon Published: 2019-09-05T04:16:48.000Z ## Abstract Constructing an organized dataset comprised of a large number of images and several captions for each image is a laborious task, which requires vast human effort. On the other hand, collecting a large number of images and sentences separately may be immensely easier. In this paper, we develop a novel data-efficient semi-supervised framework for training an image captioning model. We leverage massive unpaired image and caption data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseudo-labels to unpaired samples via Generative Adversarial Networks to learn the joint distribution of image and caption. To evaluate, we construct scarcely-paired COCO dataset, a modified version of MS COCO caption dataset. The empirical results show the effectiveness of our method compared to several strong baselines, especially when the amount of the paired samples are scarce. ## 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. - [Image Captioning on MS-COCO](): Image Captioning; MS-COCO; CIDEr: 55.2. ## Original sources and implementation [Original paper]() [PDF]()