To See is to Believe: Prompting GPT-4V for Better Visual Instruction
  Tuning
Authors: name: Junke Wang, name: Lingchen Meng, name: Zejia Weng, name: Bo He, name: Zuxuan Wu, name: Yu-Gang Jiang

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.