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

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 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 …

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 …

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 …

Cross-modality person re-identification (cm-ReID) is a challenging but key technology for
intelligent video analysis. Existing works mainly focus on learning modality-shared …

We develop an approach to learning visual representations that embraces multimodal data,
driven by a combination of intra-and inter-modal similarity preservation objectives. Unlike …

Cross-modal retrieval between videos and texts has attracted growing attentions due to the
rapid emergence of videos on the web. The current dominant approach is to learn a joint …