What is the Visual Cognition Gap between Humans and Multimodal LLMs?

Xu Cao, Bolin Lai, Wenqian Ye, Yunsheng Ma, Joerg Heintz, Jintai Chen, Jianguo Cao, James M. Rehg·June 14, 2024

Summary

The paper investigates the Visual Cognition Gap between humans and Multimodal Large Language Models (MLLMs) in abstract visual reasoning, using datasets like MaRs-VQA and VCog-Bench. These benchmarks assess zero-shot performance, revealing a gap between model and human abilities, particularly in tasks inspired by Raven's Progressive Matrices and Wechsler Intelligence Scale for Children. Comparative studies with open-source and closed-source MLLMs, such as GPT-4o and Claude 3 Opus, show that while larger models perform better, they still fall short of human performance in tasks requiring complex visual comprehension and abstract reasoning. The authors aim to stimulate progress by publicly releasing the benchmark and code, encouraging the development of models with enhanced visual reasoning capabilities that more closely mimic human performance.

Key findings

8

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the Visual Cognition Gap between Humans and Multimodal Large Language Models (LLMs) by introducing a new abstract visual reasoning benchmark called VCog-Bench . This benchmark combines visual question-answering data from existing datasets like RAVEN and CVR, along with a new dataset called MaRs-VQA, designed by psychologists specifically for abstract visual reasoning evaluation . The goal is to rigorously evaluate the performance of 16 existing MLLMs and their variants, as well as human performance, under a zero-shot inference setting . The paper highlights the gap between MLLMs and humans in abstract visual reasoning tasks and provides insights into the deficiencies of MLLMs, inspiring further investigations . This problem of evaluating and improving AI systems' capabilities for human-like visual understanding is not entirely new but is addressed comprehensively and innovatively in this paper through the introduction of the VCog-Bench benchmark .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the Visual Cognition Gap between Humans and Multimodal Large Language Models (LLMs) . The research focuses on exploring the differences in visual cognition abilities between humans and multimodal LLMs, particularly in areas such as abstract reasoning, analogical reasoning, and systematic reasoning . The study seeks to investigate how LLMs perform in visual cognition tasks compared to human cognitive abilities, aiming to identify the strengths and limitations of LLMs in understanding visual scenes and making inferences based on partial information .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "What is the Visual Cognition Gap between Humans and Multimodal LLMs?" introduces several new ideas, methods, and models in the field of visual cognition and multimodal learning. Here are some key proposals from the paper with references to specific details :

  1. Object-Centric Learning: The paper discusses generalization and robustness implications in object-centric learning, emphasizing the importance of object-centric reasoning in visual cognition .

  2. Multimodal Reasoning via Thought Chains: It explores the concept of multimodal reasoning via thought chains for science question answering, highlighting the role of thought chains in enhancing multimodal reasoning capabilities .

  3. Abstract Visual Reasoning: The paper delves into deep learning methods for abstract visual reasoning, presenting a survey on Raven's Progressive Matrices and the importance of visual abstract reasoning in cognitive tasks .

  4. Closed-Source and Open-Source Models: It compares closed-source Multimodal Large Language Models (MLLMs) like Claude 3 family, GPT-4V, GPT-4o with open-source models, providing insights into their performance in visual reasoning tasks .

  5. Zero-Shot Inference Results: The paper presents zero-shot inference results of different closed-source MLLMs using multiple images as inputs, showcasing the accuracy and performance of these models in visual reasoning tasks .

  6. Neural Correlates of Visual and Verbal Cognitive Styles: It discusses the neural correlates of visual and verbal cognitive styles, shedding light on how different cognitive styles impact visual cognition and reasoning abilities .

  7. Emergent Abilities of Large Language Models: The paper explores the emergent abilities of large language models, emphasizing their potential in enhancing reasoning capabilities and cognitive tasks .

  8. Failure on Theory-of-Mind Tasks: It addresses the limitations of large language models in theory-of-mind tasks, highlighting areas where these models may struggle in understanding human-like intuitive behavior and reasoning biases .

These proposals contribute to advancing the understanding of the visual cognition gap between humans and multimodal Large Language Models, offering insights into new methods and models for enhancing visual reasoning and cognitive tasks. The paper "What is the Visual Cognition Gap between Humans and Multimodal LLMs?" introduces novel characteristics and advantages compared to previous methods in the field of visual cognition and multimodal learning. Here are some key points highlighting the characteristics and advantages of the proposed methods with references to specific details from the paper:

  1. Object-Centric Learning: The paper emphasizes object-centric learning, which focuses on generalization and robustness implications in visual cognition. This approach enhances interpretability compared to end-to-end closed-source models, providing a structured way to break down problems .

  2. Multimodal Reasoning via Thought Chains: The method of multimodal reasoning via thought chains for science question answering offers enhanced problem-solving capabilities. It leverages CoT reasoning to improve zero-shot learning performance in solving Abstract Visual Reasoning (AVR) problems .

  3. Deep Learning Methods for Abstract Visual Reasoning: The paper presents deep learning methods for abstract visual reasoning, particularly focusing on Raven's Progressive Matrices. This survey on emerging research directions in abstract visual reasoning contributes to advancing the field .

  4. Zero-Shot Inference Results: The paper provides zero-shot inference results of different closed-source Multimodal Large Language Models (MLLMs) using multiple images as inputs. These results demonstrate the accuracy and performance of models like GPT-4V and GPT-4o in visual reasoning tasks .

  5. Neural Correlates of Visual and Verbal Cognitive Styles: The discussion on the neural correlates of visual and verbal cognitive styles sheds light on how different cognitive styles impact visual cognition and reasoning abilities. This understanding is crucial for developing effective models for visual reasoning tasks .

  6. Emergent Abilities of Large Language Models: The exploration of the emergent abilities of large language models highlights their potential in enhancing reasoning capabilities and cognitive tasks. This insight contributes to the development of more advanced models for visual cognition tasks .

These characteristics and advantages underscore the innovative approaches proposed in the paper, offering valuable insights into improving visual cognition and multimodal learning through advanced methods and models.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of visual cognition and multimodal large language models (LLMs). Noteworthy researchers in this area include:

  • John McCarthy
  • José Hernández-Orallo
  • Arthur R Jensen
  • Mikołaj Małki´nski and Jacek Ma´ndziuk
  • Dedre Gentner
  • David Wechsler and Habuku Kodama
  • Jean Raven
  • François Chollet
  • David Barrett, Felix Hill, Adam Santoro, Ari Morcos, and Timothy Lillicrap
  • Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, and Song-Chun Zhu

The key to the solution mentioned in the paper involves the development of new and challenging benchmarks based on the theory of visual cognition to assess and improve AI systems' capabilities for human-like visual understanding. This includes focusing on complex abstract reasoning and logical reasoning abilities related to fluid intelligence, which are areas where current research has been limited .


How were the experiments in the paper designed?

The experiments in the paper were designed in two parts :

  1. End-to-End Zero-Shot Inference: This involved using multiple images as input, including a question image and several option images, to guide the Multimodal Language Models (MLLMs) to decompose the problem into predefined structures before generating answers based on all available information. Models like Claude 3 family, GPT-4V, and GPT-4o were tested for this task, with results showing that even state-of-the-art closed-source MLLMs perform worse than humans in all Abstract Visual Reasoning (AVR) tasks.
  2. Use of Visual Language Models (VLMs): The second part of the experiment focused on using VLMs and GPT-4o to extract option descriptions for solving AVR problems in MaRs-VQA and RAVEN datasets. The study excluded the CVR dataset due to the complexity of shapes. The results indicated that large-scale VLMs achieved comparable results to GPT-4o in MaRs-VQA and RAVEN, with Gemini Pro 1.5 outperforming GPT-4o on the RAVEN dataset.

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MaRs-VQA dataset, which contains 1,440 image instances designed by psychologists and is the largest dataset for abstract visual reasoning (AVR) evaluation . The code for the study is not explicitly mentioned as open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study conducted experiments focusing on the Visual Cognition Gap between Humans and Multimodal Large Language Models (LLMs) . The experiments were divided into two parts, with the first part involving end-to-end zero-shot inference using multiple images as input to test models like Claude 3 family, GPT-4V, and GPT-4o . The results indicated that even state-of-the-art closed-source MLLMs performed worse than humans in all Abstract Visual Reasoning (AVR) tasks .

Furthermore, the study explored the use of Visual Language Models (VLMs) and GPT-4o to extract option descriptions for solving AVR problems in MaRs-VQA and RAVEN datasets . The results showed that large-scale VLMs achieved comparable performance to GPT-4o in these tasks . However, the overall performance of these models remains limited, as human subjects outperformed them due to the reliance on both verbal reasoning and visual cognition to solve AVR problems .

The references cited in the paper also contribute to the scientific rigor of the study, providing a solid foundation for the hypotheses being tested. These references include works by researchers such as John McCarthy, José Hernández-Orallo, and François Chollet, among others . The inclusion of these references enhances the credibility of the study and supports the validity of the scientific hypotheses being investigated .


What are the contributions of this paper?

The contributions of the paper "What is the Visual Cognition Gap between Humans and Multimodal LLMs?" include:

  • Providing insights into generalization and robustness implications in object-centric learning .
  • Exploring object-centric slot diffusion in the context of visual cognition .
  • Investigating the implications of abstract visual reasoning in neural networks .
  • Introducing open-access abstract reasoning items for adolescents and adults .
  • Analyzing differences in recognizing emotions between autistic and non-autistic adults .
  • Conducting an item response theory analysis of the matrix reasoning item bank .
  • Moving developmental research online and comparing in-lab and web-based studies .
  • Enhancing reasoning, OCR, and world knowledge in LLAVA-NEXT .
  • Investigating the emergent abilities of large language models .
  • Exploring the impact of valence on self-referential processing in adolescents and young adults .
  • Introducing a benchmark for compositional visual reasoning .
  • Studying the neural correlates of visual and verbal cognitive styles .
  • Analyzing children's reasoning about continuous causal processes .
  • Evaluating the progress of deep learning for visual relational concepts .
  • Investigating abstract visual reasoning through an algebraic approach for solving Raven's Progressive Matrices .
  • Introducing a self-configurable model to solve various abstract visual reasoning problems .
  • Exploring the compositional nature of visual objects and learning to compose visual relations .
  • Providing zero-shot inference results of different closed-source multimodal language models .

What work can be continued in depth?

Further research in the field of visual cognition and multimodal large language models (LLMs) can be expanded in several areas:

  • Exploration of Complex Matrix Reasoning Tasks: Current state-of-the-art Multimodal LLMs and Vision-Language Models (VLMs) like GPT-4o and LLaVA-1.6 show basic understanding of Audio-Visual Reasoning (AVR) tasks but face challenges with complex matrix reasoning tasks. This indicates the necessity for more development and investigation in this domain .
  • Enhancing Visual Reasoning Capabilities: While Large Language Models (LLMs) have shown success in language-based reasoning tasks, there is limited research on Multimodal LLMs and visual cognition. Future studies could focus on improving the visual reasoning abilities of these models, especially in abstract visual reasoning tasks that require high-level cognitive skills .
  • Benchmarking for Visual Cognition: Developing new and challenging benchmarks based on the theory of visual cognition is crucial to assess and enhance AI systems' capabilities for human-like visual understanding. These benchmarks should focus on testing MLLMs' cognitive abilities in complex abstract reasoning and logical reasoning tasks related to fluid intelligence .
  • Zero-Shot Abstract Visual Reasoning: Research can delve deeper into zero-shot abstract visual reasoning, where models can solve problems without explicit learning from large-scale data. Evaluating Multimodal LLMs on their zero-shot AVR capability and comparing their performance with human intelligence can provide valuable insights for advancing these models .
  • Generalization and Robustness: Investigating the generalization and robustness implications in object-centric learning and object-centric slot diffusion can contribute to improving the performance and reliability of Multimodal LLMs in various visual tasks .

By focusing on these areas, researchers can advance the understanding and capabilities of Multimodal LLMs in visual cognition tasks, paving the way for more sophisticated and human-like AI systems.

Tables

1

Introduction
Background
Emergence of Multimodal Large Language Models (MLLMs)
Importance of abstract visual reasoning in AI development
Objective
To quantify the gap between humans and MLLMs in visual cognition tasks
To analyze the performance of open-source and closed-source models
Encourage model development for enhanced visual reasoning
Method
Data Collection
Datasets
MaRs-VQA: Abstract Visual Reasoning VQA dataset
VCog-Bench: Comprehensive benchmark for visual cognition comparison
Zero-shot Performance Evaluation
Benchmarking MLLMs on Raven's Progressive Matrices and WISC tasks
Data Preprocessing
Standardization of input and output formats for model evaluation
Cleaning and preprocessing of visual stimuli for model understanding
Model Selection
GPT-4o: Open-source MLLM
Claude 3 Opus: Closed-source MLLM
Comparative analysis of different model architectures
Performance Analysis
Quantitative analysis of model accuracy and human performance
Identification of tasks where models underperform
Results and Discussion
Presentation of the visual cognition gap findings
Analysis of factors contributing to the gap (e.g., model size, reasoning complexity)
Limitations and potential explanations for model performance
Public Release and Future Directions
Open-source benchmark and code for research community
Recommendations for model improvements and future research
Potential implications for AI development and human-AI collaboration
Conclusion
Summary of key findings and contributions
Importance of closing the visual cognition gap for real-world applications
Call to action for researchers to address this challenge
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What is the authors' goal by making the benchmark and code publicly available?
What does the paper focus on in terms of the comparison between humans and MLLMs?
How do the larger MLLMs, like GPT-4 and Claude 3 Opus, perform compared to humans in abstract visual reasoning tasks?
Which datasets are used to evaluate the Visual Cognition Gap in the study?

What is the Visual Cognition Gap between Humans and Multimodal LLMs?

Xu Cao, Bolin Lai, Wenqian Ye, Yunsheng Ma, Joerg Heintz, Jintai Chen, Jianguo Cao, James M. Rehg·June 14, 2024

Summary

The paper investigates the Visual Cognition Gap between humans and Multimodal Large Language Models (MLLMs) in abstract visual reasoning, using datasets like MaRs-VQA and VCog-Bench. These benchmarks assess zero-shot performance, revealing a gap between model and human abilities, particularly in tasks inspired by Raven's Progressive Matrices and Wechsler Intelligence Scale for Children. Comparative studies with open-source and closed-source MLLMs, such as GPT-4o and Claude 3 Opus, show that while larger models perform better, they still fall short of human performance in tasks requiring complex visual comprehension and abstract reasoning. The authors aim to stimulate progress by publicly releasing the benchmark and code, encouraging the development of models with enhanced visual reasoning capabilities that more closely mimic human performance.
Mind map
Comparative analysis of different model architectures
Claude 3 Opus: Closed-source MLLM
GPT-4o: Open-source MLLM
Benchmarking MLLMs on Raven's Progressive Matrices and WISC tasks
VCog-Bench: Comprehensive benchmark for visual cognition comparison
MaRs-VQA: Abstract Visual Reasoning VQA dataset
Identification of tasks where models underperform
Quantitative analysis of model accuracy and human performance
Model Selection
Zero-shot Performance Evaluation
Datasets
Encourage model development for enhanced visual reasoning
To analyze the performance of open-source and closed-source models
To quantify the gap between humans and MLLMs in visual cognition tasks
Importance of abstract visual reasoning in AI development
Emergence of Multimodal Large Language Models (MLLMs)
Call to action for researchers to address this challenge
Importance of closing the visual cognition gap for real-world applications
Summary of key findings and contributions
Potential implications for AI development and human-AI collaboration
Recommendations for model improvements and future research
Open-source benchmark and code for research community
Limitations and potential explanations for model performance
Analysis of factors contributing to the gap (e.g., model size, reasoning complexity)
Presentation of the visual cognition gap findings
Performance Analysis
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Public Release and Future Directions
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Emergence of Multimodal Large Language Models (MLLMs)
Importance of abstract visual reasoning in AI development
Objective
To quantify the gap between humans and MLLMs in visual cognition tasks
To analyze the performance of open-source and closed-source models
Encourage model development for enhanced visual reasoning
Method
Data Collection
Datasets
MaRs-VQA: Abstract Visual Reasoning VQA dataset
VCog-Bench: Comprehensive benchmark for visual cognition comparison
Zero-shot Performance Evaluation
Benchmarking MLLMs on Raven's Progressive Matrices and WISC tasks
Data Preprocessing
Standardization of input and output formats for model evaluation
Cleaning and preprocessing of visual stimuli for model understanding
Model Selection
GPT-4o: Open-source MLLM
Claude 3 Opus: Closed-source MLLM
Comparative analysis of different model architectures
Performance Analysis
Quantitative analysis of model accuracy and human performance
Identification of tasks where models underperform
Results and Discussion
Presentation of the visual cognition gap findings
Analysis of factors contributing to the gap (e.g., model size, reasoning complexity)
Limitations and potential explanations for model performance
Public Release and Future Directions
Open-source benchmark and code for research community
Recommendations for model improvements and future research
Potential implications for AI development and human-AI collaboration
Conclusion
Summary of key findings and contributions
Importance of closing the visual cognition gap for real-world applications
Call to action for researchers to address this challenge
Key findings
8

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the Visual Cognition Gap between Humans and Multimodal Large Language Models (LLMs) by introducing a new abstract visual reasoning benchmark called VCog-Bench . This benchmark combines visual question-answering data from existing datasets like RAVEN and CVR, along with a new dataset called MaRs-VQA, designed by psychologists specifically for abstract visual reasoning evaluation . The goal is to rigorously evaluate the performance of 16 existing MLLMs and their variants, as well as human performance, under a zero-shot inference setting . The paper highlights the gap between MLLMs and humans in abstract visual reasoning tasks and provides insights into the deficiencies of MLLMs, inspiring further investigations . This problem of evaluating and improving AI systems' capabilities for human-like visual understanding is not entirely new but is addressed comprehensively and innovatively in this paper through the introduction of the VCog-Bench benchmark .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the Visual Cognition Gap between Humans and Multimodal Large Language Models (LLMs) . The research focuses on exploring the differences in visual cognition abilities between humans and multimodal LLMs, particularly in areas such as abstract reasoning, analogical reasoning, and systematic reasoning . The study seeks to investigate how LLMs perform in visual cognition tasks compared to human cognitive abilities, aiming to identify the strengths and limitations of LLMs in understanding visual scenes and making inferences based on partial information .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "What is the Visual Cognition Gap between Humans and Multimodal LLMs?" introduces several new ideas, methods, and models in the field of visual cognition and multimodal learning. Here are some key proposals from the paper with references to specific details :

  1. Object-Centric Learning: The paper discusses generalization and robustness implications in object-centric learning, emphasizing the importance of object-centric reasoning in visual cognition .

  2. Multimodal Reasoning via Thought Chains: It explores the concept of multimodal reasoning via thought chains for science question answering, highlighting the role of thought chains in enhancing multimodal reasoning capabilities .

  3. Abstract Visual Reasoning: The paper delves into deep learning methods for abstract visual reasoning, presenting a survey on Raven's Progressive Matrices and the importance of visual abstract reasoning in cognitive tasks .

  4. Closed-Source and Open-Source Models: It compares closed-source Multimodal Large Language Models (MLLMs) like Claude 3 family, GPT-4V, GPT-4o with open-source models, providing insights into their performance in visual reasoning tasks .

  5. Zero-Shot Inference Results: The paper presents zero-shot inference results of different closed-source MLLMs using multiple images as inputs, showcasing the accuracy and performance of these models in visual reasoning tasks .

  6. Neural Correlates of Visual and Verbal Cognitive Styles: It discusses the neural correlates of visual and verbal cognitive styles, shedding light on how different cognitive styles impact visual cognition and reasoning abilities .

  7. Emergent Abilities of Large Language Models: The paper explores the emergent abilities of large language models, emphasizing their potential in enhancing reasoning capabilities and cognitive tasks .

  8. Failure on Theory-of-Mind Tasks: It addresses the limitations of large language models in theory-of-mind tasks, highlighting areas where these models may struggle in understanding human-like intuitive behavior and reasoning biases .

These proposals contribute to advancing the understanding of the visual cognition gap between humans and multimodal Large Language Models, offering insights into new methods and models for enhancing visual reasoning and cognitive tasks. The paper "What is the Visual Cognition Gap between Humans and Multimodal LLMs?" introduces novel characteristics and advantages compared to previous methods in the field of visual cognition and multimodal learning. Here are some key points highlighting the characteristics and advantages of the proposed methods with references to specific details from the paper:

  1. Object-Centric Learning: The paper emphasizes object-centric learning, which focuses on generalization and robustness implications in visual cognition. This approach enhances interpretability compared to end-to-end closed-source models, providing a structured way to break down problems .

  2. Multimodal Reasoning via Thought Chains: The method of multimodal reasoning via thought chains for science question answering offers enhanced problem-solving capabilities. It leverages CoT reasoning to improve zero-shot learning performance in solving Abstract Visual Reasoning (AVR) problems .

  3. Deep Learning Methods for Abstract Visual Reasoning: The paper presents deep learning methods for abstract visual reasoning, particularly focusing on Raven's Progressive Matrices. This survey on emerging research directions in abstract visual reasoning contributes to advancing the field .

  4. Zero-Shot Inference Results: The paper provides zero-shot inference results of different closed-source Multimodal Large Language Models (MLLMs) using multiple images as inputs. These results demonstrate the accuracy and performance of models like GPT-4V and GPT-4o in visual reasoning tasks .

  5. Neural Correlates of Visual and Verbal Cognitive Styles: The discussion on the neural correlates of visual and verbal cognitive styles sheds light on how different cognitive styles impact visual cognition and reasoning abilities. This understanding is crucial for developing effective models for visual reasoning tasks .

  6. Emergent Abilities of Large Language Models: The exploration of the emergent abilities of large language models highlights their potential in enhancing reasoning capabilities and cognitive tasks. This insight contributes to the development of more advanced models for visual cognition tasks .

These characteristics and advantages underscore the innovative approaches proposed in the paper, offering valuable insights into improving visual cognition and multimodal learning through advanced methods and models.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of visual cognition and multimodal large language models (LLMs). Noteworthy researchers in this area include:

  • John McCarthy
  • José Hernández-Orallo
  • Arthur R Jensen
  • Mikołaj Małki´nski and Jacek Ma´ndziuk
  • Dedre Gentner
  • David Wechsler and Habuku Kodama
  • Jean Raven
  • François Chollet
  • David Barrett, Felix Hill, Adam Santoro, Ari Morcos, and Timothy Lillicrap
  • Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, and Song-Chun Zhu

The key to the solution mentioned in the paper involves the development of new and challenging benchmarks based on the theory of visual cognition to assess and improve AI systems' capabilities for human-like visual understanding. This includes focusing on complex abstract reasoning and logical reasoning abilities related to fluid intelligence, which are areas where current research has been limited .


How were the experiments in the paper designed?

The experiments in the paper were designed in two parts :

  1. End-to-End Zero-Shot Inference: This involved using multiple images as input, including a question image and several option images, to guide the Multimodal Language Models (MLLMs) to decompose the problem into predefined structures before generating answers based on all available information. Models like Claude 3 family, GPT-4V, and GPT-4o were tested for this task, with results showing that even state-of-the-art closed-source MLLMs perform worse than humans in all Abstract Visual Reasoning (AVR) tasks.
  2. Use of Visual Language Models (VLMs): The second part of the experiment focused on using VLMs and GPT-4o to extract option descriptions for solving AVR problems in MaRs-VQA and RAVEN datasets. The study excluded the CVR dataset due to the complexity of shapes. The results indicated that large-scale VLMs achieved comparable results to GPT-4o in MaRs-VQA and RAVEN, with Gemini Pro 1.5 outperforming GPT-4o on the RAVEN dataset.

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MaRs-VQA dataset, which contains 1,440 image instances designed by psychologists and is the largest dataset for abstract visual reasoning (AVR) evaluation . The code for the study is not explicitly mentioned as open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study conducted experiments focusing on the Visual Cognition Gap between Humans and Multimodal Large Language Models (LLMs) . The experiments were divided into two parts, with the first part involving end-to-end zero-shot inference using multiple images as input to test models like Claude 3 family, GPT-4V, and GPT-4o . The results indicated that even state-of-the-art closed-source MLLMs performed worse than humans in all Abstract Visual Reasoning (AVR) tasks .

Furthermore, the study explored the use of Visual Language Models (VLMs) and GPT-4o to extract option descriptions for solving AVR problems in MaRs-VQA and RAVEN datasets . The results showed that large-scale VLMs achieved comparable performance to GPT-4o in these tasks . However, the overall performance of these models remains limited, as human subjects outperformed them due to the reliance on both verbal reasoning and visual cognition to solve AVR problems .

The references cited in the paper also contribute to the scientific rigor of the study, providing a solid foundation for the hypotheses being tested. These references include works by researchers such as John McCarthy, José Hernández-Orallo, and François Chollet, among others . The inclusion of these references enhances the credibility of the study and supports the validity of the scientific hypotheses being investigated .


What are the contributions of this paper?

The contributions of the paper "What is the Visual Cognition Gap between Humans and Multimodal LLMs?" include:

  • Providing insights into generalization and robustness implications in object-centric learning .
  • Exploring object-centric slot diffusion in the context of visual cognition .
  • Investigating the implications of abstract visual reasoning in neural networks .
  • Introducing open-access abstract reasoning items for adolescents and adults .
  • Analyzing differences in recognizing emotions between autistic and non-autistic adults .
  • Conducting an item response theory analysis of the matrix reasoning item bank .
  • Moving developmental research online and comparing in-lab and web-based studies .
  • Enhancing reasoning, OCR, and world knowledge in LLAVA-NEXT .
  • Investigating the emergent abilities of large language models .
  • Exploring the impact of valence on self-referential processing in adolescents and young adults .
  • Introducing a benchmark for compositional visual reasoning .
  • Studying the neural correlates of visual and verbal cognitive styles .
  • Analyzing children's reasoning about continuous causal processes .
  • Evaluating the progress of deep learning for visual relational concepts .
  • Investigating abstract visual reasoning through an algebraic approach for solving Raven's Progressive Matrices .
  • Introducing a self-configurable model to solve various abstract visual reasoning problems .
  • Exploring the compositional nature of visual objects and learning to compose visual relations .
  • Providing zero-shot inference results of different closed-source multimodal language models .

What work can be continued in depth?

Further research in the field of visual cognition and multimodal large language models (LLMs) can be expanded in several areas:

  • Exploration of Complex Matrix Reasoning Tasks: Current state-of-the-art Multimodal LLMs and Vision-Language Models (VLMs) like GPT-4o and LLaVA-1.6 show basic understanding of Audio-Visual Reasoning (AVR) tasks but face challenges with complex matrix reasoning tasks. This indicates the necessity for more development and investigation in this domain .
  • Enhancing Visual Reasoning Capabilities: While Large Language Models (LLMs) have shown success in language-based reasoning tasks, there is limited research on Multimodal LLMs and visual cognition. Future studies could focus on improving the visual reasoning abilities of these models, especially in abstract visual reasoning tasks that require high-level cognitive skills .
  • Benchmarking for Visual Cognition: Developing new and challenging benchmarks based on the theory of visual cognition is crucial to assess and enhance AI systems' capabilities for human-like visual understanding. These benchmarks should focus on testing MLLMs' cognitive abilities in complex abstract reasoning and logical reasoning tasks related to fluid intelligence .
  • Zero-Shot Abstract Visual Reasoning: Research can delve deeper into zero-shot abstract visual reasoning, where models can solve problems without explicit learning from large-scale data. Evaluating Multimodal LLMs on their zero-shot AVR capability and comparing their performance with human intelligence can provide valuable insights for advancing these models .
  • Generalization and Robustness: Investigating the generalization and robustness implications in object-centric learning and object-centric slot diffusion can contribute to improving the performance and reliability of Multimodal LLMs in various visual tasks .

By focusing on these areas, researchers can advance the understanding and capabilities of Multimodal LLMs in visual cognition tasks, paving the way for more sophisticated and human-like AI systems.

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