Language BERTs

Language BERTs, particularly those extended for vision-language tasks (VL-BERTs), aim to improve multimodal understanding by leveraging the power of pre-trained transformer models. Current research focuses on enhancing VL-BERT architectures to better handle temporal information in videos (e.g., by incorporating trajectory-word alignments) and adapting them to perform multi-step tasks through graphical user interfaces. These advancements are significant because they enable more robust and versatile applications, such as improved video understanding and the development of AI agents capable of interacting with complex visual interfaces.