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 …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Answering visual queries is a complex task that requires both visual processing and
reasoning. End-to-end models, the dominant approach for this task, do not explicitly …

Large-scale task planning is a major challenge. Recent work exploits large language
models (LLMs) directly as a policy and shows surprisingly interesting results. This paper …

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

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 …

Most text-driven human motion generation methods employ sequential modeling
approaches, eg, transformer, to extract sentence-level text representations automatically and …

Video question answering requires the models to understand and reason about both the
complex video and language data to correctly derive the answers. Existing efforts have been …

Image captioning is one of the primary goals in computer vision which aims to automatically
generate natural descriptions for images. Intuitively, human visual system can notice some …

Automatic radiology report generation has been an attracting research problem towards
computer-aided diagnosis to alleviate the workload of doctors in recent years. Deep learning …