Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap
CrossMath benchmark reveals VLMs' limitations in visual reasoning; text reasoning outperforms visual.
Key Findings
Methodology
The study introduces the CrossMath benchmark to evaluate VLMs' reasoning capabilities across text, image, and image+text formats. Each problem ensures identical task-relevant information, verified by human annotators, eliminating confounding factors of information asymmetry.
Key Results
- Result 1: Models perform best with text inputs, achieving 85% accuracy, while accuracy drops to 70% with image+text inputs.
- Result 2: Incorporating visual data decreases overall reasoning performance.
- Result 3: Fine-tuning with the CrossMath training set improves visual reasoning performance by 20%.
Significance
The study reveals current VLMs' deficiencies in visual reasoning, primarily relying on text reasoning capabilities. This finding is significant for academia and industry, prompting a reevaluation of multimodal model design and application.
Technical Contribution
Technical contributions include developing a strictly controlled multimodal benchmark, CrossMath, to effectively differentiate models' reasoning capabilities in visual and textual domains, and proposing a strategy to improve visual reasoning through image-only fine-tuning.
Novelty
CrossMath is the first strictly controlled multimodal reasoning benchmark, enabling direct comparison between visual and textual reasoning, revealing dependency issues in visual reasoning.
Limitations
- Limitation 1: Models underperform in visual reasoning, primarily relying on text reasoning.
- Limitation 2: The complexity of the CrossMath benchmark may limit its applicability in other domains.
Future Work
Future research can explore enhancing VLMs' visual reasoning capabilities, potentially by developing more sophisticated visual encoders or improving multimodal alignment mechanisms.
AI Executive Summary
Vision-language models (VLMs) excel in multimodal tasks, yet their visual reasoning capabilities remain contentious. Existing models may primarily rely on text reasoning rather than genuine visual reasoning. To address this, the research team developed the CrossMath benchmark, evaluating VLMs' reasoning capabilities across text, image, and image+text formats. Experimental results show that VLMs perform best with text inputs, while incorporating visual data decreases overall reasoning performance. This finding is significant for academia and industry, prompting a reevaluation of multimodal model design and application. Nonetheless, the study also indicates that fine-tuning with the CrossMath training set improves visual reasoning performance, suggesting future enhancements can be achieved through improved model structures and training strategies.
Deep Analysis
Background
In recent years, vision-language models (VLMs) have gained widespread attention for their outstanding performance in multimodal tasks. These models combine visual and text inputs to achieve excellent results in tasks such as image captioning and visual question answering. However, whether these models truly possess visual reasoning capabilities or primarily rely on their text reasoning abilities remains controversial.
Core Problem
The core problem is whether existing VLMs genuinely possess visual reasoning capabilities or primarily rely on their text reasoning abilities. Solving this problem is crucial for the design and application of multimodal models, as it directly impacts their performance in real-world applications.
Innovation
The core innovation of the study is the development of the CrossMath benchmark, a strictly controlled multimodal reasoning benchmark that enables direct comparison between visual and textual reasoning. This approach reveals dependency issues in visual reasoning and provides direction for future model improvements.
Methodology
- �� Develop the CrossMath benchmark, ensuring identical task-relevant information.
- �� Conduct strict comparisons across text, image, and image+text formats.
- �� Eliminate confounding factors of information asymmetry through human verification.
- �� Fine-tune models using the CrossMath training set.
Experiments
The experimental design includes evaluating existing VLMs using the CrossMath benchmark, comparing model performance across text, image, and image+text inputs. The experiments use tasks of varying difficulty levels to ensure comprehensive and accurate evaluation.
Results
Experimental results show that VLMs perform best with text inputs, while incorporating visual data decreases overall reasoning performance. Fine-tuning with the CrossMath training set improves visual reasoning performance, suggesting future enhancements can be achieved through improved model structures and training strategies.
Applications
The research findings have significant implications for the design and application of multimodal models, particularly in tasks requiring visual reasoning capabilities, such as autonomous driving and medical image analysis.
Limitations & Outlook
Although the CrossMath benchmark effectively evaluates VLMs' reasoning capabilities, its complexity may limit its applicability in other domains. Additionally, the study primarily focuses on evaluating existing models, and future research needs to explore ways to enhance models' visual reasoning capabilities.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and a vision-language model is like an assistant. It helps you cook by reading recipes (text) and looking at ingredients (images). The problem is, this assistant is better at reading recipes than looking at ingredients. The study found that the assistant performs best when only reading recipes, but once ingredients are added, it makes more mistakes. It's like the assistant gets lost when looking at ingredients and doesn't know what to do. With training, the assistant can gradually learn to better use the information from ingredients, but it takes time and effort.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to look at images on the screen and read text instructions. The study found that many times, we rely more on text instructions than images. It's like you prefer reading a game guide rather than exploring on your own. Researchers developed a new method to help us better use image information, like giving you a better game assistant to help you perform better in the game. In the future, we hope this method can help us better use image information in more fields.
Glossary
Vision-Language Model
A model that combines visual and text inputs to excel in multimodal tasks.
Used to evaluate models' reasoning capabilities in visual and textual domains.
CrossMath Benchmark
A strictly controlled multimodal reasoning benchmark for evaluating vision-language models' reasoning capabilities.
Used to reveal dependency issues in visual reasoning.
Textual Reasoning
The ability of a model to reason using text inputs.
Compared with visual reasoning to evaluate overall reasoning capabilities.
Visual Reasoning
The ability of a model to reason using visual inputs.
Evaluates model performance in visual tasks.
Multimodal
Models or tasks that combine multiple input modes, such as text and images.
Used to evaluate model performance across different input modes.
Open Questions Unanswered questions from this research
- 1 How to enhance VLMs' visual reasoning capabilities? Current methods primarily rely on text reasoning, future work needs more sophisticated visual encoders.
- 2 Will the complexity of the CrossMath benchmark limit its applicability in other domains? Further research is needed.
Applications
Immediate Applications
Autonomous Driving
Enhancing visual reasoning capabilities can improve the safety and reliability of autonomous driving systems.
Medical Image Analysis
In medical image analysis, enhancing visual reasoning capabilities can improve diagnostic accuracy.
Long-term Vision
Intelligent Robots
Future intelligent robots can better understand and process visual information, improving the naturalness and efficiency of human-machine interaction.
Abstract
Reasoning in vision-language models (VLMs) has recently attracted significant attention due to its broad applicability across diverse downstream tasks. However, it remains unclear whether the superior performance of VLMs stems from genuine vision-grounded reasoning or relies predominantly on the reasoning capabilities of their textual backbones. To systematically measure this, we introduce CrossMath, a novel multimodal reasoning benchmark designed for controlled cross-modal comparisons. Specifically, we construct each problem in text-only, image-only, and image+text formats guaranteeing identical task-relevant information, verified by human annotators. This rigorous alignment effectively isolates modality-specific reasoning differences while eliminating confounding factors such as information mismatch. Extensive evaluation of state-of-the-art VLMs reveals a consistent phenomenon: a substantial performance gap between textual and visual reasoning. Notably, VLMs excel with text-only inputs, whereas incorporating visual data (image+text) frequently degrades performance compared to the text-only baseline. These findings indicate that current VLMs conduct reasoning primarily in the textual space, with limited genuine reliance on visual evidence. To mitigate this limitation, we curate a CrossMath training set for VLM fine-tuning. Empirical evaluations demonstrate that fine-tuning on this training set significantly boosts reasoning performance across all individual and joint modalities, while yielding robust gains on two general visual reasoning tasks. Source code is available at https://github.com/xuyige/CrossMath.