C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning

• URL: http://arxiv.org/abs/2405.12752v2
• Date: Tue, 2 Jul 2024 06:05:24 GMT
• Title: C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning
• Authors: Ji Ma, Wei Suo, Peng Wang, Yanning Zhang,
• Abstract summary: 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. A contrastive learning module is introduced to further boost the VLIT data generation capability of the LVLMs.
• Score: 45.233150828317164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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