Image–text matching is a crucial branch in multimedia retrieval which relies on learning inter-
modal correspondences. Most existing methods focus on global or local correspondence …

Exploring the semantic correspondence of image-text pairs is significant as it bridges vision
and language. Most prior works focus on global semantic alignment or local semantic …

Image–text retrieval plays a considerable role in associating vision and language. Existing
mainstream approaches focus on fine-grained alignment while ignoring the influence of …

The objective of visual question answering (VQA) is to adequately comprehend a question
and identify relevant contents in an image that can provide an answer. Existing approaches …

Image–text matching has attracted much attention as one of the visual-linguistic tasks. Most
of the existing methods tend to concentrate on single-level semantic similarity by global …

Existing mainstream image-text matching methods usually measure the relevance of image-
text pairs by capturing and aggregating the affinities between textual words and visual …

Image-text retrieval aims to capture the semantic correspondence between images and
texts, which serves as a foundation and crucial component in multi-modal recommendations …

Image-text matching is a vital yet challenging task in the field of vision and language. Unlike
previous methods that usually adopt a symmetrical network to independently embed images …

As a fundamental multimodal task, image-text retrieval bridges the gap between vision and
language. Current mainstream methods exploit attention mechanisms to discover potential …

The cross-modal drone image-text (DIT) retrieval task involves using either text or drone
images as queries to retrieve relevant drone images or corresponding text. The primary …