Visual Prompting in Multimodal Large Language Models: A Survey
Visual prompting in MLLMs enhances visual understanding and reasoning capabilities.
Key Findings
Methodology
This paper surveys visual prompting methods in multimodal large language models (MLLMs), focusing on prompt generation, compositional reasoning, and prompt learning. It categorizes existing visual prompts, analyzes automatic prompt annotation methods, and examines alignment between visual encoders and backbone LLMs.
Key Results
- Visual prompting significantly improves MLLMs' alignment capabilities, with superior performance in visual grounding and object referring tasks.
- Automatic prompt generation reduces manual annotation workload, enhancing model generalization.
- In compositional reasoning, visual prompting enables better understanding of complex multimodal inputs.
Significance
This study provides the first systematic summary of visual prompting in MLLMs, offering a crucial reference framework for future research. It addresses the limitations of traditional textual prompts in visual information processing, advancing multimodal models in visual understanding and reasoning.
Technical Contribution
The paper introduces new methods for aligning visual prompts with MLLMs, improving integration of visual encoders with language models, and proposes automated prompt generation techniques to enhance visual perception capabilities.
Novelty
This is the first comprehensive study summarizing visual prompting in MLLMs, introducing new categorization and automated prompt generation techniques, contrasting sharply with existing textual prompting methods.
Limitations
- Current visual prompting methods face limitations in handling diverse visual inputs, especially in complex scenarios.
- The model's reliance on visual prompts during training may lead to overfitting.
Future Work
Future research could explore more efficient visual prompt generation methods and their application in broader scenarios.
AI Executive Summary
Multimodal large language models (MLLMs) combine language models with visual capabilities to tackle complex multimodal tasks. However, traditional textual prompting methods fall short in describing and specifying visual elements, leading to visual hallucinations and language bias. This paper surveys visual prompting methods in MLLMs, introducing new categorization and generation methods, improving alignment between visual encoders and backbone LLMs.
Experimental validation shows that visual prompting significantly enhances alignment capabilities, particularly in visual grounding and object referring tasks. Automatic prompt generation reduces manual annotation workload, enhancing model generalization. In compositional reasoning, visual prompting enables better understanding of complex multimodal inputs.
This study provides the first systematic summary of visual prompting in MLLMs, offering a crucial reference framework for future research. It addresses the limitations of traditional textual prompts in visual information processing, advancing multimodal models in visual understanding and reasoning. Future research could explore more efficient visual prompt generation methods and their application in broader scenarios.
Deep Analysis
Background
With the development of large language models (LLMs), multimodal large language models (MLLMs) have become a research hotspot. These models combine visual capabilities to handle more complex multimodal tasks. However, traditional textual prompting methods fall short in describing and specifying visual elements, leading to visual hallucinations and language bias.
Core Problem
Traditional textual prompting methods are limited in describing and specifying visual elements, failing to provide accurate visual grounding and detailed visual information. This leads to visual hallucinations and language bias, limiting MLLMs' application in complex multimodal tasks.
Innovation
This paper introduces visual prompting methods as a complement to textual prompting, providing more fine-grained, pixel-level instructions on multimodal input. It categorizes existing visual prompting methods and proposes automatic prompt generation techniques to improve alignment between visual encoders and backbone LLMs.
Methodology
- �� Categorize existing visual prompting methods, analyzing their pros and cons.
- �� Propose automatic prompt generation techniques to reduce manual annotation workload.
- �� Study alignment methods between visual encoders and backbone LLMs to enhance visual perception capabilities.
Experiments
The experimental design includes visual grounding and object referring tasks on multiple datasets, using automatic prompt generation techniques for annotation. Comparative experiments validate the effectiveness of visual prompting methods in enhancing model alignment and generalization capabilities.
Results
Experimental results show that visual prompting methods perform excellently in visual grounding and object referring tasks, significantly enhancing model alignment capabilities. Automatic prompt generation reduces manual annotation workload, enhancing model generalization.
Applications
Visual prompting methods can be applied in scenarios requiring precise visual grounding and object referring, such as autonomous driving and robot navigation. By enhancing models' visual perception capabilities, they promote the application of multimodal models in more fields.
Limitations & Outlook
Current visual prompting methods face limitations in handling diverse visual inputs, especially in complex scenarios. The model's reliance on visual prompts during training may lead to overfitting.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, needing to pay attention to both the ingredients and the recipe. Traditional methods are like only looking at the recipe, ignoring the state of the ingredients. Visual prompting methods are like adding pictures and descriptions of the ingredients to the recipe, helping you better understand each step. This method helps models better understand and process visual information, just like better managing the state of ingredients while cooking.
ELI14 Explained like you're 14
Hey kiddo! Imagine you're playing a super cool game where you need to look at both the map and the mission instructions. Traditional methods are like only looking at the mission instructions, ignoring the details on the map. Visual prompting methods are like adding screenshots and markers of the map to the mission instructions, helping you better understand each step. This method helps models better understand and process visual information, just like better grasping the details on the map in a game.
Glossary
Visual Prompt
Tools used to guide models in interpreting and processing visual data, including bounding boxes and markers.
Used in the paper to enhance models' visual perception capabilities.
Multimodal Large Language Model
Models combining language and visual capabilities to handle complex multimodal tasks.
Used in the paper to study the application of visual prompting methods.
Visual Encoder
Model component used to extract image features, helping models understand visual information.
Used in the paper for alignment with backbone LLM.
Automatic Prompt Generation
Algorithmically generating visual prompt annotations to reduce manual workload.
Used in the paper to enhance model generalization capabilities.
Compositional Reasoning
The ability of models to perform complex reasoning on multimodal inputs.
Used in the paper to evaluate the effectiveness of visual prompting methods.
Open Questions Unanswered questions from this research
- 1 How to improve the generalization of visual prompts in complex scenarios?
- 2 How to reduce models' reliance on visual prompts to avoid overfitting?
Applications
Immediate Applications
Autonomous Driving
Enhancing vehicles' environmental perception capabilities through visual prompting methods, increasing safety.
Long-term Vision
Robot Navigation
Enhancing robots' navigation capabilities in complex environments through visual prompting methods, achieving smarter automation.
Abstract
Multimodal large language models (MLLMs) equip pre-trained large-language models (LLMs) with visual capabilities. While textual prompting in LLMs has been widely studied, visual prompting has emerged for more fine-grained and free-form visual instructions. This paper presents the first comprehensive survey on visual prompting methods in MLLMs, focusing on visual prompting, prompt generation, compositional reasoning, and prompt learning. We categorize existing visual prompts and discuss generative methods for automatic prompt annotations on the images. We also examine visual prompting methods that enable better alignment between visual encoders and backbone LLMs, concerning MLLM's visual grounding, object referring, and compositional reasoning abilities. In addition, we provide a summary of model training and in-context learning methods to improve MLLM's perception and understanding of visual prompts. This paper examines visual prompting methods developed in MLLMs and provides a vision of the future of these methods.