2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Introduces a multimodal textbook dataset to enhance VLMs' performance in knowledge reasoning tasks.
Key Findings
Methodology
This study introduces a multimodal textbook dataset using an LLM-constructed knowledge taxonomy to systematically collect instructional videos. Visual keyframes, ASR, and OCR texts are extracted and organized into an interleaved corpus.
Key Results
- In tasks like ScienceQA and MathVista, pretrained models showed significant improvement in reasoning capabilities.
- Compared to existing datasets, the new dataset offers better image-text alignment and logical coherence.
- In few-shot learning, models can better utilize visual and textual cues for task solving.
Significance
By introducing a high-quality multimodal textbook dataset, this research addresses issues of low knowledge density and loose image-text relations in existing datasets, providing richer foundational knowledge for VLM pretraining.
Technical Contribution
Proposes a method to extract high-quality multimodal data from instructional videos, significantly enhancing image-text alignment and logical coherence, offering new possibilities for VLM pretraining.
Novelty
First to systematically use instructional videos to construct a multimodal textbook dataset, providing a more natural interleaved corpus to enhance models' knowledge reasoning abilities.
Limitations
- The dataset primarily relies on English instructional videos, which may limit performance in multilingual environments.
- Some scenes in videos may contain irrelevant information, affecting the dataset's purity.
Future Work
Future work could expand to multilingual datasets and explore applications in more tasks.
AI Executive Summary
Vision-Language Models (VLMs) have excelled in tasks like image captioning, dialogue, and visual question answering, but existing datasets suffer from low knowledge density and loose image-text relations.
This study introduces a new multimodal textbook dataset by extracting keyframes, ASR, and OCR texts from instructional videos, creating a high-quality interleaved corpus. The dataset excels in tasks like ScienceQA and MathVista, significantly enhancing reasoning capabilities.
Despite its strong performance, the dataset faces language limitations and scene purity issues. Future research could explore multilingual dataset construction and applications.
Deep Analysis
Background
Vision-Language Models have rapidly advanced, largely due to improvements in large language models and diverse multimodal training corpora. However, existing datasets often suffer from low knowledge density and loose image-text relations.
Core Problem
Existing interleaved datasets lack sufficient knowledge density and image-text relations, impacting model performance in complex reasoning tasks.
Innovation
This study innovatively uses instructional videos to construct a multimodal textbook dataset, offering higher knowledge density and better image-text alignment.
Methodology
- �� Use LLM to construct a knowledge taxonomy
- �� Systematically collect instructional videos
- �� Extract keyframes, ASR, and OCR texts
- �� Organize into an interleaved corpus
Experiments
Experiments used ScienceQA and MathVista datasets to evaluate model performance in knowledge reasoning tasks, comparing effects of different datasets.
Results
The new dataset excels in reasoning tasks, particularly in knowledge-intensive tasks, significantly enhancing model reasoning capabilities.
Applications
The dataset can enhance VLM applications in education, automated question answering, and more.
Limitations & Outlook
The dataset is primarily based on English videos, which may limit performance in other languages.
Plain Language Accessible to non-experts
Imagine watching an educational video where a teacher explains and writes on a board. Our research extracts these images and texts to create a large book, allowing computers to understand and learn this knowledge like humans.
ELI14 Explained like you're 14
Hey there! Imagine you're in an online class, and the teacher is explaining all sorts of cool stuff in a video. What we do is turn these videos into a super textbook so computers can learn just like you! Isn't that awesome?
Glossary
Vision-Language Model
A model capable of processing both visual and language information, used for tasks like image captioning and visual question answering.
Used to enhance model performance in multimodal tasks.
ASR (Automatic Speech Recognition)
Technology that converts speech into text, used to extract spoken content from videos.
Used to extract textual information from instructional videos.
OCR (Optical Character Recognition)
Technology that recognizes text in images and converts it into editable text.
Used to extract written content from video keyframes.
Keyframe
An image frame representing important scenes in a video.
Used to extract significant visual information from videos.
Interleaved Corpus
A corpus where images and text alternate, aiming to improve multimodal understanding.
Used to construct the multimodal textbook dataset.
Open Questions Unanswered questions from this research
- 1 How to construct similar datasets in multilingual environments to enhance model language adaptability.
- 2 How to handle irrelevant information in videos to improve dataset purity.
Applications
Immediate Applications
Education Sector
This dataset can help educational software better understand and generate instructional content, improving teaching quality.
Long-term Vision
Cross-Language Multimodal Models
Develop models capable of handling multilingual, multimodal inputs, promoting global education resource sharing.
Abstract
Compared to image-text pair data, interleaved corpora enable Vision-Language Models (VLMs) to understand the world more naturally like humans. However, such existing datasets are crawled from webpage, facing challenges like low knowledge density, loose image-text relations, and poor logical coherence between images. On the other hand, the internet hosts vast instructional videos (e.g., online geometry courses) that are widely used by humans to learn foundational subjects, yet these valuable resources remain underexplored in VLM training. In this paper, we introduce a high-quality \textbf{multimodal textbook} corpus with richer foundational knowledge for VLM pretraining. It collects over 2.5 years of instructional videos, totaling 22,000 class hours. We first use an LLM-proposed taxonomy to systematically gather instructional videos. Then we progressively extract and refine visual (keyframes), audio (ASR), and textual knowledge (OCR) from the videos, and organize as an image-text interleaved corpus based on temporal order. Compared to its counterparts, our video-centric textbook offers more coherent context, richer knowledge, and better image-text alignment. Experiments demonstrate its superb pretraining performance, particularly in knowledge- and reasoning-intensive tasks like ScienceQA and MathVista. Moreover, VLMs pre-trained on our textbook exhibit outstanding interleaved context awareness, leveraging visual and textual cues in their few-shot context for task solving. Our code are available at https://github.com/DAMO-NLP-SG/multimodal_textbook.