Visual Instruction Tuning

Visual instruction tuning refines large multimodal models by training them to follow instructions given alongside images or videos, significantly improving their ability to perform complex vision-language tasks. Current research focuses on improving data quality and diversity through gamified crowdsourcing, synthetic data generation, and careful data selection strategies, often employing techniques like LoRA for efficient fine-tuning and exploring various model architectures like LLaVA and its variants. This approach is crucial for advancing multimodal AI, enabling more robust and versatile models with applications ranging from image captioning and question answering to complex reasoning tasks in diverse domains like medicine and robotics.