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 …

The remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

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 …

Recent studies have shown promising results on utilizing large pre-trained image-language
models for video question answering. While these image-language models can efficiently …

Prompt-based learning has emerged as a successful paradigm in natural language
processing, where a single general-purpose language model can be instructed to perform …

Abstract To build Video Question Answering (VideoQA) systems capable of assisting
humans in daily activities, seeking answers from long-form videos with diverse and complex …

Understanding verbs is crucial to modelling how people and objects interact with each other
and the environment through space and time. Recently, state-of-the-art video-language …

Contrastive learning-based video-language representation learning approaches, eg, CLIP,
have achieved outstanding performance, which pursue semantic interaction upon pre …

The last several years have witnessed remarkable progress in video-and-language (VidL)
understanding. However, most modern VidL approaches use complex and specialized …

Existing visual question answering methods often suffer from cross-modal spurious
correlations and oversimplified event-level reasoning processes that fail to capture event …