Visual Instruction

Visual instruction tuning focuses on enhancing multimodal large language models (MLLMs) by training them to follow instructions that incorporate both textual and visual information. Current research emphasizes creating high-quality, diverse datasets of visual instructions, often leveraging LLMs themselves for data generation, and developing model architectures that effectively integrate visual and textual cues, including techniques like contrastive learning and region-of-interest focusing. This field is significant because it pushes the boundaries of multimodal understanding and reasoning, leading to improved performance in various applications such as image captioning, question answering, and even robotic control.