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 …

For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Transformer with self-attention has led to the revolutionizing of natural language processing
field, and recently inspires the emergence of Transformer-style architecture design with …

In recent years, we have witnessed significant performance boost in the image captioning
task based on vision-language pre-training (VLP). Scale is believed to be an important factor …

This paper presents a detailed study of improving vision features and develops an improved
object detection model for vision language (VL) tasks. Compared to the most widely used …

While language tasks are naturally expressed in a single, unified, modeling framework, ie,
generating sequences of tokens, this has not been the case in computer vision. As a result …

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Abstract Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for
computer vision tasks, while the self-attention computation in Transformer scales …