This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

Connecting Vision and Language plays an essential role in Generative Intelligence. For this
reason, large research efforts have been devoted to image captioning, ie describing images …

Inspired by the strong ties between vision and language, the two intimate human sensing
and communication modalities, our paper aims to explore the generation of 3D human full …

Recent years have witnessed the resurgence of knowledge engineering which is featured
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …

Recent progress on visual question answering has explored the merits of grid features for
vision language tasks. Meanwhile, transformer-based models have shown remarkable …

Attention mechanisms are widely used in current encoder/decoder frameworks of image
captioning, where a weighted average on encoded vectors is generated at each time step to …

The limited availability of annotated data often hinders real-world applications of machine
learning. To efficiently learn from small quantities of multimodal data, we leverage the …

Self-supervised learning has become increasingly important to leverage the abundance of
unlabeled data available on platforms like YouTube. Whereas most existing approaches …

Abstract Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel
compositional concepts based on learned concepts such as attribute-object combinations …

Analyzing land cover changes with multitemporal remote sensing (RS) images is crucial for
environmental protection and land planning. In this article, we explore RS image change …