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 …

Most existing image-text matching methods adopt triplet loss as the optimization objective,
and choosing a proper negative sample for the triplet of< anchor, positive, negative> is …

We study the visual semantic embedding problem for image-text matching. Most existing
work utilizes a tailored cross-attention mechanism to perform local alignment across the two …

Cross-modal metric learning is a prominent research topic that bridges the semantic
heterogeneity between vision and language. Existing methods frequently utilize simple …

Prior image-text matching methods have shown remarkable performance on many
benchmark datasets, but most of them overlook the bias in the dataset, which exists in intra …

Image-text matching is a fundamental task to bridge vision and language. The critical
challenge lies in accurately learning the semantic similarity between these two …

In this paper, we propose a novel visual Semantic-Spatial Self-Highlighting Network (termed
3SHNet) for high-precision, high-efficiency and high-generalization image–sentence …

Image-text matching, as a fundamental cross-modal task, bridges vision and language. The
key challenge lies in accurately learning the semantic similarity of these two heterogeneous …

In this paper, we present and study a new instance-level retrieval task: PointCloud-Text
Matching~(PTM), which aims to find the exact cross-modal instance that matches a given …