Vision Language Transformer

Vision-Language Transformers (VLTs) integrate visual and textual information to enable computers to understand and reason about images and text simultaneously. Current research focuses on improving VLT efficiency through techniques like token pruning and cross-modal alignment, as well as enhancing their capabilities in tasks such as visual question answering, referring segmentation, and video captioning, often employing transformer-in-transformer architectures or masked autoencoders. These advancements are significant because they improve the accuracy and efficiency of multimodal AI systems, leading to broader applications in areas like human-robot interaction and scene understanding. Addressing issues like bias and improving interpretability remain key challenges.