With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Image-text matching, as a fundamental task, bridges the gap between vision and language.
The key of this task is to accurately measure similarity between these two modalities. Prior …

Image-text matching plays a critical role in bridging the vision and language, and great
progress has been made by exploiting the global alignment between image and sentence …

Image-text retrieval is a fundamental and vital task in multi-media retrieval and has received
growing attention since it connects heterogeneous data. Previous methods that perform well …

Image-text matching has been a hot research topic bridging the vision and language areas.
It remains challenging because the current representation of image usually lacks global …

Abstract Visual Semantic Embedding (VSE) is a dominant approach for vision-language
retrieval, which aims at learning a deep embedding space such that visual data are …

The key of image and sentence matching is to accurately measure the visual-semantic
similarity between an image and a sentence. However, most existing methods make use of …

In this paper, we study the problem of image-text matching. Inferring the latent semantic
alignment between objects or other salient stuff (eg snow, sky, lawn) and the corresponding …