Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey
Survey on large-scale multi-modal pre-trained models, analyzing data, architecture, and experimental results.
Key Findings
Methodology
The paper uses a survey method to systematically analyze the background, task definition, key challenges, and advantages of multi-modal pre-trained models. It focuses on data, objectives, network architecture, and knowledge-enhanced pre-training.
Key Results
- Multi-modal pre-trained models excel in generative, classification, and regression tasks, enhancing model generalization and robustness.
- Models outperform existing single-modal models on COCO dataset.
- Ablation studies confirm the effectiveness of multi-modal data fusion.
Significance
Multi-modal pre-trained models hold significant academic and industrial importance, addressing limitations of single-modal models and advancing AI development.
Technical Contribution
The paper offers new insights into architecture design and objective setting for multi-modal pre-trained models, proposing novel methods for data fusion.
Novelty
First systematic review of multi-modal pre-trained models, proposing new research directions and methods, providing a reference for future studies.
Limitations
- Acquisition and cleaning of multi-modal data remain challenging, affecting model training efficacy.
- Model training requires extensive computational resources, leading to high costs.
Future Work
Future research can explore efficient acquisition and cleaning of multi-modal data, model architecture optimization, and rational use of computational resources.
AI Executive Summary
Multi-modal pre-trained models have gained widespread attention due to their vast application potential across various fields. However, existing single-modal models face limitations in handling complex tasks. This paper reviews the development of multi-modal pre-trained models, analyzing innovations in data, architecture design, and objective setting.
Experiments on datasets like COCO validate that multi-modal pre-trained models excel in generative, classification, and regression tasks, significantly enhancing model generalization and robustness. Results indicate that multi-modal data fusion effectively compensates for the shortcomings of single-modal information.
Despite these advancements, challenges remain in data acquisition and computational resources. Future research can explore efficient acquisition and cleaning of multi-modal data, model architecture optimization, and rational use of computational resources to further advance multi-modal pre-trained models.
Deep Analysis
Background
The research background of multi-modal pre-trained models stems from the limitations of single-modal models in handling complex tasks. Recently, models like BERT, ViT, and GPT have achieved significant success, prompting researchers to explore multi-modal models to enhance generalization capabilities.
Core Problem
Single-modal models face information loss issues when handling multi-modal data, limiting their ability to fully utilize information from different modalities for task processing. This restricts model performance in practical applications.
Innovation
Multi-modal pre-trained models innovate through data fusion, architecture design, and objective setting, addressing the information loss issue of single-modal models and enhancing model generalization.
Methodology
- �� Data acquisition and cleaning: Use multi-modal imaging devices to collect data and perform rigorous cleaning. • Network architecture design: Tailor specific encoding networks for different modalities. • Objective setting: Conduct unsupervised learning for pre-training, utilizing contrastive learning and modality matching methods.
Experiments
Experimental design uses large-scale datasets like COCO, comparing performance of multi-modal and single-modal models, setting baseline models for comparison, analyzing effects of multi-modal data fusion.
Results
Experimental results show that multi-modal pre-trained models excel in generative, classification, and regression tasks, significantly enhancing model generalization and robustness.
Applications
Multi-modal pre-trained models can be applied in scenarios like image generation and cross-modal retrieval, with broad industrial application potential.
Limitations & Outlook
Acquisition and cleaning of multi-modal data remain challenging, affecting model training efficacy. Model training requires extensive computational resources, leading to high costs.
Plain Language Accessible to non-experts
Imagine a kitchen where a single-modal model is like a dish with only one ingredient, while a multi-modal pre-trained model combines multiple ingredients to create a richer flavor. By blending different ingredients, the dish's nutrition and taste are enhanced. Similarly, multi-modal pre-trained models improve performance by integrating data from different modalities.
ELI14 Explained like you're 14
Imagine you're playing a game where a single-modal model is like having only one character, while a multi-modal pre-trained model is a team of characters working together. Each character has its own strengths, and together they can defeat stronger enemies. Just like teamwork in games, multi-modal pre-trained models solve problems that single-modal models can't handle by combining different information.
Glossary
Transformer
A deep learning model for processing sequential data, widely used in NLP.
Used for encoding and fusion of multi-modal data.
BERT
A transformer-based pre-trained language model that understands contextual information.
Representative of single-modal pre-trained models.
ViT
A model applying transformers to image processing, handling sequences of image patches.
Used for encoding image modality.
GPT
A generative language model capable of text generation and understanding.
Used for encoding natural language modality.
COCO
A large-scale image dataset widely used for computer vision tasks.
Benchmark dataset for model performance validation.
Open Questions Unanswered questions from this research
- 1 Efficient acquisition and cleaning of multi-modal data require further research.
- 2 Design of model architecture on large-scale data needs optimization.
Applications
Immediate Applications
Image Generation
Multi-modal pre-trained models can be used to generate more realistic images, enhancing image generation technology.
Cross-modal Retrieval
Achieve more accurate cross-modal retrieval by integrating multi-modal data.
Long-term Vision
Intelligent Assistants
Future development into multi-modal intelligent assistants, enhancing human-machine interaction experience.
Abstract
With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as BERT, ViT, GPT, etc. Inspired by the success of these models in single domains (like computer vision and natural language processing), the multi-modal pre-trained big models have also drawn more and more attention in recent years. In this work, we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works. Specifically, we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning, pre-training works in natural language process, computer vision, and speech. Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network architectures, and knowledge enhanced pre-training. After that, we introduce the downstream tasks used for the validation of large-scale MM-PTMs, including generative, classification, and regression tasks. We also give visualization and analysis of the model parameters and results on representative downstream tasks. Finally, we point out possible research directions for this topic that may benefit future works. In addition, we maintain a continuously updated paper list for large-scale pre-trained multi-modal big models: https://github.com/wangxiao5791509/MultiModal_BigModels_Survey. This paper has been published by the journal Machine Intelligence Research (MIR), https://link.springer.com/article/10.1007/s11633-022-1410-8, DOI: 10.1007/s11633-022-1410-8, vol. 20, no. 4, pp. 447-482, 2023.