The paper focuses on a vision-language model called InstructBLIP and explores the process of instruction tuning. The authors collect 26 datasets and categorize them for instruction tuning and zero-shot evaluation. They also introduce a method called instruction-aware visual feature extraction. The results show that InstructBLIP achieves the best performance among all models, surpassing BLIP-2 and Flamingo. When fine-tuned for specific tasks, InstructBLIP exhibits exceptional accuracy, such as 90.7% on the ScienceQA IMG task. Through qualitative comparisons, the study highlights InstructBLIP's superiority over other multimodal models, demonstrating its importance in the field of vision-language tasks.