The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …

The long-standing endeavor of relating the textual and the visual domain recently underwent
a pivotal breakthrough, as OpenAI released CLIP. This model distinguishes how well an …

Pretrained models have produced great success in both Computer Vision (CV) and Natural
Language Processing (NLP). This progress leads to learning joint representations of vision …

With the explosive growth of multi-modal information on the Internet, the multi-modal
knowledge graph (MMKG) has become an important research topic in knowledge graphs to …

Traditionally, the task of cross-modal retrieval is tackled through joint embedding. However,
the global matching used in joint embedding methods often fails to effectively describe …

Despite the recent developments in the field of cross-modal retrieval, there has been less
research focusing on low-resource languages due to the lack of manually annotated …

Existing multilingual video corpus moment retrieval (mVCMR) methods are mainly based on
a two-stream structure. The visual stream utilizes the visual content in the video to estimate …

Current research on cross-modal retrieval is mostly English-oriented, as the availability of a
large number of English-oriented human-labeled vision-language corpora. In order to break …

Image-text retrieval is a challenging cross-modal task that arouses much attention. While the
traditional methods cannot break down the barriers between different modalities, Vision …

By exploiting the cross-modal attention, cross-BERT methods have achieved state-of-the-art
accuracy in cross-modal retrieval. Nevertheless, the heavy text-image interactions in the …