Current state-of-the-art image-text matching methods implicitly align the visual-semantic
fragments, like regions in images and words in sentences, and adopt cross-attention …

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 …

Recently, image-text matching has attracted more and more attention from academia and
industry, which is fundamental to understanding the latent correspondence across visual …

Image-sentence retrieval has attracted extensive research attention in multimedia and
computer vision due to its promising application. The key issue lies in jointly learning the …

With the rapid development of science and technology, all types of mixed media contain
large amounts of data. Traditional single multimedia data can no longer satisfy daily …

Image-text retrieval aims to take the text (image) query to retrieve the semantically relevant
images (texts), which is fundamental and critical in the search system, online shopping, and …

Image-text retrieval is a central problem for understanding the semantic relationship
between vision and language, and serves as the basis for various visual and language …

Most of the image-text retrieval methods carry out accurate results using fine-grained
features for feature alignment. However, extracting the robustness features while …

Unleashing the power of image-text matching in real-world applications is hampered by
noisy correspondence. Manually curating high-quality datasets is expensive and time …

Text-guided image retrieval integrates reference image and text feedback as a multimodal
query to search the image corresponding to user intention. Recent approaches employ multi …