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Existing visual instruction tuning methods typically prompt large languagemodels with textual descriptions to generate instruction-following data.Despite the promising performance achieved, these descriptions are derived fromimage annotations, which are oftentimes coarse-grained. Furthermore, theinstructions might even contradict the visual content without observing theentire visual context. To address this challenge, we introduce a fine-grainedvisual instruction dataset, LVIS-Instruct4V, which contains 220K visuallyaligned and context-aware instructions produced by prompting the powerfulGPT-4V with images from LVIS. Through experimental validation and case studies,we demonstrate that high-quality visual instructional data could improve theperformance of LLaVA-1.5, a state-of-the-art large multimodal model, across awide spectrum of benchmarks by clear margins. Notably, by simply replacing theLLaVA-Instruct with our LVIS-Instruct4V, we achieve better results than LLaVAon most challenging LMM benchmarks, e.g., LLaVA$^w$ (76.7 vs. 70.7) and MM-Vet(40.2 vs. 35.4). We release our data and model athttps://github.com/X2FD/LVIS-INSTRUCT4V.

To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning