Image-text matching, a bridge connecting image and language, is an important task, which
generally learns a holistic cross-modal embedding to achieve a high-quality semantic …

Although the attention mechanism in transformers has proven successful in image-text
retrieval tasks, most transformer models suffer from a large number of parameters. Inspired …

Image-text retrieval, a fundamental cross-modal task, performs similarity reasoning for
images and texts. The primary challenge for image-text retrieval is cross-modal semantic …

The Natural Language Interface to Databases (NLIDB) empowers non-technical users with
database access through intuitive natural language (NL) interactions. Advanced …

Image-text retrieval serves as a bridge connecting vision and language. Mainstream modal
cross matching methods can effectively perform cross-modal interactions with high …

Due to the inconsistency in feature representations between different modalities, known as
the “Heterogeneous gap”, image–text retrieval (ITR) is a challenging task. To bridge this …

Matching model is essential for Image-Text Retrieval framework. Existing research usually
train the model with a triplet loss and explore various strategy to retrieve hard negative …

Multi-modal pre-training and knowledge discovery are two important research topics in multi-
modal machine learning. Nevertheless, none of existing works make attempts to link …

Multimodal understanding aims at constructing semantic correlations among modalities of
data while performing various downstream tasks. As one of the primary multimodal …

This letter presents a novel approach to enhance image-text matching by incorporating word
sense disambiguation (WSD) within the text encoder. Our method explicitly models the …