Vision and Language

Vision and language research aims to create computational models that understand and generate both visual and textual information, bridging the gap between human perception and language processing. Current research focuses on improving the accuracy and efficiency of vision-and-language models (VLMs), exploring architectures like transformers and MLPs, and addressing challenges such as multimodal grounding, bias mitigation, and cross-lingual capabilities. These advancements are significant for various applications, including image captioning, visual question answering, and more generally, enabling more robust and nuanced human-computer interaction.