Key Points
1. The paper introduces VISION-FLAN, a diverse visual instruction tuning dataset comprising 187 tasks and 1,664,261 instances, addressing challenges in task diversity and avoiding annotation errors and bias in existing VLM frameworks.
2. Proposes a two-stage instruction tuning framework that outperforms the traditional single-stage visual instruction tuning framework, achieving state-of-the-art performance across a wide range of multi-modal evaluation benchmarks.
3. Investigates the role of GPT-4 synthesized data and human-labeled data, revealing that a minimal quantity of GPT-4 synthesized data can effectively align VLM responses with human-preferred formats.
4. Reveals that increasing the number of human-labeled tasks in visual instruction tuning substantially enhances VLMs' capabilities across extensive evaluation benchmarks.
5. Analyzes the impact of GPT-4 synthesized data on VLMs, finding that it modulates the responses towards human-preferred formats rather than substantially enhancing VLMs' capabilities.
6. Introduces a two-stage instruction tuning pipeline, demonstrating that minimal GPT-4 synthesized data effectively aligns VLMs' responses with human preferences while avoiding hallucination and catastrophic forgetting.
7. Shows that diverse human-labeled tasks within VISION-FLAN are essential for improving the capabilities of VLMs, with increasing the number of tasks enhancing model performance.
8. Demonstrates that GPT-4 synthesized data does not substantially improve VLMs' performance on comprehensive evaluation benchmarks and can introduce hallucination and bias into the models.
9. Confirms that visual instruction tuning mainly enhances the ability of large-language models (LLMs) to understand visual features and discusses future research directions to extend VISION-FLAN and explore alternative VLM architectures.
Summary
The research paper addresses challenges in existing vision-language models (VLMs) and introduces VISION-FLAN, a diverse visual instruction tuning dataset, and a two-stage instruction tuning framework. The challenges include lacking task diversity in pretraining and visual instruction tuning, and annotation error and bias in GPT-4 synthesized instruction tuning data. VISION-FLAN consists of 187 diverse tasks and 1,664,261 instances sourced from academic datasets, aiming to address these challenges. The two-stage instruction tuning framework significantly outperforms traditional single-stage tuning and provides insights into the effects of GPT-4 synthesized data on VLMs' capabilities.
The paper also highlights the existing VLM frameworks, such as LLaVA and SVIT, and their limitations in overcoming the challenges. It discusses previous attempts to address these challenges, such as fine-tuning VLMs on instruction tuning datasets covering more tasks and using GPT-4 synthesized data. The paper emphasizes the importance of task diversity and provides in-depth analyses to understand visual instruction tuning, demonstrating that increasing the number of human-labeled tasks can enhance VLMs' capabilities.
Performance Evaluation and Impact Analysis
Furthermore, the paper evaluates the performance of VISION-FLAN BASE and VISION-FLAN CHAT on comprehensive evaluation benchmarks, highlighting their achievements in reducing hallucination and catastrophic forgetting. It also explores the impact of GPT-4 synthesized data on VLMs' capabilities and human-preference alignment, finding that a minimal quantity of GPT-4 synthesized data can effectively align VLM responses with human preferences. Additionally, the paper discusses the effects of different training strategies and the contributions of human-labeled and GPT-4 synthesized data in visual instruction tuning.
Overall, the paper presents an extensive dataset, VISION-FLAN, and a two-stage instruction tuning framework that significantly improves VLMs' capabilities. It also provides valuable insights into the impact of GPT-4 synthesized data and the importance of task diversity in visual instruction tuning. The work opens up avenues for future research, such as extending the dataset to include multilingual tasks and exploring vision-language tasks involving multiple images or videos.
Reference: https://arxiv.org/abs/2402.116...