Vision Language Instruction Tuning

Vision-language instruction tuning (VLIT) aims to enhance large multimodal models' ability to understand and respond to instructions involving both visual and textual information. Current research focuses on improving efficiency through techniques like lightweight adapters, quantized models, and mixture-of-experts architectures, often leveraging teacher-student training paradigms to adapt pre-trained models to specific tasks with limited data. This approach is particularly impactful for specialized domains like semiconductor analysis where labeled data is scarce, enabling cost-effective and secure deployment of powerful vision-language models on consumer hardware. The resulting models demonstrate improved performance on various tasks, including visual question answering and image captioning, and offer significant potential for diverse applications.