  This article aims to provide the information retrieval community with some
reflections on recent advances in retrieval learning by analyzing the
reproducibility of image-text retrieval models. Due to the increase of
multimodal data over the last decade, image-text retrieval has steadily become
a major research direction in the field of information retrieval. Numerous
researchers train and evaluate image-text retrieval algorithms using benchmark
datasets such as MS-COCO and Flickr30k. Research in the past has mostly focused
on performance, with multiple state-of-the-art methodologies being suggested in
a variety of ways. According to their assertions, these techniques provide
improved modality interactions and hence more precise multimodal
representations. In contrast to previous works, we focus on the reproducibility
of the approaches and the examination of the elements that lead to improved
performance by pretrained and nonpretrained models in retrieving images and
text. To be more specific, we first examine the related reproducibility
concerns and explain why our focus is on image-text retrieval tasks. Second, we
systematically summarize the current paradigm of image-text retrieval models
and the stated contributions of those approaches. Third, we analyze various
aspects of the reproduction of pretrained and nonpretrained retrieval models.
To complete this, we conducted ablation experiments and obtained some
influencing factors that affect retrieval recall more than the improvement
claimed in the original paper. Finally, we present some reflections and
challenges that the retrieval community should consider in the future. Our
source code is publicly available at
https://github.com/WangFei-2019/Image-text-Retrieval.
