Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data. Despite the promising performance achieved, these descriptions are derived from image annotations, which are oftentimes coarse-grained. Furthermore, the instructions might even contradict the visual content without observing the entire visual context. To address this challenge, we introduce a fine-grained visual instruction dataset, LVIS-Instruct4V, which contains 220K visually aligned and context-aware instructions produced by prompting the powerful GPT-4V with images from LVIS. Through experimental validation and case studies, we demonstrate that high-quality visual instructional data could improve the performance of LLaVA-1.5, a state-of-the-art large multimodal model, across a wide spectrum of benchmarks by clear margins. Notably, by simply replacing the LLaVA-Instruct with our LVIS-Instruct4V, we achieve better results than LLaVA on 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 at https://github.com/X2FD/LVIS-INSTRUCT4V.

翻译：现有视觉指令微调方法通常通过文本描述提示大语言模型生成指令遵循数据。尽管取得了令人瞩目的性能，但这些描述源于图像标注，往往过于粗糙。此外，在不观察完整视觉内容的情况下生成的指令甚至可能与视觉信息相矛盾。为解决这一挑战，我们引入细粒度视觉指令数据集LVIS-Instruct4V，其中包含22万条通过使用LVIS图像提示强大GPT-4V模型生成的视觉对齐且上下文感知的指令。通过实验验证与案例研究，我们证明高质量视觉指令数据能够显著提升当前最先进的多模态大模型LLaVA-1.5在广泛基准测试中的性能。值得注意的是，仅通过将LLaVA-Instruct替换为我们的LVIS-Instruct4V，我们在最具挑战性的多模态大模型基准测试（如LLaVA$^w$（76.7 vs 70.7）和MM-Vet（40.2 vs 35.4））上便取得了优于LLaVA的结果。我们已在https://github.com/X2FD/LVIS-INSTRUCT4V开源数据与模型。