VIGC: Visual Instruction Generation and Correction (2308.12714v3)

Abstract: The integration of visual encoders and LLMs has driven recent progress in multimodal LLMs (MLLMs). However, the scarcity of high-quality instruction-tuning data for vision-language tasks remains a challenge. The current leading paradigm, such as LLaVA, relies on language-only GPT-4 to generate data, which requires pre-annotated image captions and detection bounding boxes, suffering from understanding image details. A practical solution to this problem would be to utilize the available multimodal LLMs (MLLMs) to generate instruction data for vision-language tasks. However, it's worth noting that the currently accessible MLLMs are not as powerful as their LLM counterparts, as they tend to produce inadequate responses and generate false information. As a solution for addressing the current issue, this paper proposes the Visual Instruction Generation and Correction (VIGC) framework that enables multimodal LLMs to generate instruction-tuning data and progressively enhance its quality on-the-fly. Specifically, Visual Instruction Generation (VIG) guides the vision-LLM to generate diverse instruction-tuning data. To ensure generation quality, Visual Instruction Correction (VIC) adopts an iterative update mechanism to correct any inaccuracies in data produced by VIG, effectively reducing the risk of hallucination. Leveraging the diverse, high-quality data generated by VIGC, we finetune mainstream models and validate data quality based on various evaluations. Experimental results demonstrate that VIGC not only compensates for the shortcomings of language-only data generation methods, but also effectively enhances the benchmark performance. The models, datasets, and code are available at https://opendatalab.github.io/VIGC.

Overview of the VIGC Framework

The paper "VIGC: Visual Instruction Generation and Correction" presents a comprehensive solution to the challenge of generating high-quality instruction-tuning data for vision-language tasks. The increasing integration of visual encoders with LLMs has led to advancements in Multimodal LLMs (MLLMs). Despite these improvements, acquiring instruction-tuning data for vision-language tasks remains significantly challenging due to its scarcity.

Motivation and Methodology

The core challenge addressed by the paper is the lack of high-quality data required for efficiently tuning MLLMs. Traditional paradigms like LLaVA rely on language-only GPT-4 models to generate data, relying on pre-annotated image captions and detection bounding boxes, constrained in their capability to understand intricate details within images. This paper proposes an alternative: utilizing the existing MLLMs themselves to autonomously generate instruction data specifically tuned for vision-language tasks.

The proposed Visual Instruction Generation and Correction (VIGC) framework is designed to enable MLLMs to generate diverse instruction-tuning data accurately while iteratively enhancing the quality of this data. Within this framework, two critical components are introduced: Visual Instruction Generation (VIG) and Visual Instruction Correction (VIC). VIG steers the vision-LLM in generating diverse instruction-tuning datasets, while VIC iteratively updates and corrects inaccuracies introduced by VIG, thereby mitigating the risk of erroneous information creation, commonly termed "hallucination".

Results and Validation

Extensive experiments were conducted to validate the effectiveness of VIGC. The paper reports that the inclusion of data generated through VIGC significantly enhances the performance of MLLM benchmarks. When trained on data generated using VIGC, mainstream models like LLaVA-7B show notable improvement, achieving performance levels that exceed that of larger models such as LLaVA-13B.

In addition to performance enhancements, VIGC provides a practical approach to alleviate challenges associated with the heavy reliance on manually annotated datasets for model tuning. The data generation process supported by VIGC effectively complements existing methods and provides a scalable, self-sufficient means of augmenting instruction datasets, proving beneficial for both current models and broader AI developments.

Implications and Future Directions

The findings pave the way for significant implications—theoretically and practically. Theoretically, VIGC can be seen as a paradigm in generating instruction-following datasets autonomously, bypassing the limitations of manual data curation. Practically, the deployment of VIGC promises more robust MLLMs, capable of better instruction-following and real-world applicability.

Looking ahead, one can anticipate further advancements in reducing hallucination phenomena and refining the VIGC approach to cover a wider array of task-specific applications. Moreover, integrating VIGC’s data generation capabilities with ongoing multimodal model training could establish an iterative improvement loop—continuously enhancing data quality and model performance simultaneously.

In conclusion, the VIGC framework stands as a strategic advancement in mitigating existing challenges within vision-language tasks, promising more efficient utilization of MLLMs that can better bridge the gap between language and visual understanding. Its ability to self-generate and refine high-quality data eliminates critical bottlenecks, potentially transforming approaches to AI training and deployment.