  Vision-Language Instruction Tuning (VLIT) is a critical training phase for
Large Vision-Language Models (LVLMs). With the improving capabilities of
open-source LVLMs, researchers have increasingly turned to generate VLIT data
by using open-source LVLMs and achieved significant progress. However, such
data generation approaches are bottlenecked by the following challenges: 1)
Since multi-modal models tend to be influenced by prior language knowledge,
directly using LVLMs to generate VLIT data would inevitably lead to low content
relevance between generated data and images. 2) To improve the ability of the
models to generate VLIT data, previous methods have incorporated an additional
training phase to boost the generative capacity. This process hurts the
generalization of the models to unseen inputs (i.e., "exposure bias" problem).
In this paper, we propose a new Content Correlated VLIT data generation via
Contrastive Learning (C3L). Specifically, we design a new content relevance
module which enhances the content relevance between VLIT data and images by
computing Image Instruction Correspondence Scores S(I2C). Moreover, a
contrastive learning module is introduced to further boost the VLIT data
generation capability of the LVLMs. A large number of automatic measures on
four benchmarks show the effectiveness of our method.
