Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor

Veedant Jain, Felipe dos Santos Alves Feitosa, Gabriel Kreiman·June 19, 2024

Summary

HumorDB is a novel image dataset introduced in the paper to investigate visual humor understanding in computer vision. The dataset consists of 3,545 image pairs with varying humor ratings, designed to challenge models in understanding subtle humor and context. Traditional vision-only models struggle, while vision-language models, particularly those incorporating large language models like LLaVA and GPT-4, exhibit better performance. The study evaluates models on tasks such as binary classification, humor ranking, and image comparison, with a focus on pretraining and the role of multimodal inputs. Human evaluations, involving 550 participants, ensure dataset reliability and highlight the importance of originality in humor perception. The dataset, released under CC BY 4.0, serves as a benchmark for humor detection and abstract concept comprehension in AI systems, with implications for content moderation and future research in the field.

Key findings

12

Paper digest

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

The paper "Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor" aims to address the challenge of understanding complex scenes involving humor, specifically in graphical form, which remains a significant challenge despite advancements in computer vision . This paper introduces HumorDB, a novel image-only dataset designed to advance visual humor understanding by emphasizing subtle visual cues that trigger humor and mitigating potential biases . While there have been emerging datasets and approaches for humor understanding in multi-modal contexts, such as videos, there is a lack of datasets focusing solely on images, which are crucial for developing visually intelligent systems in the future . Therefore, the paper's focus on humor understanding through images presents a new and important problem in the field of computer vision and humor perception .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to advancing visual humor understanding through the creation of a novel image-only dataset called HumorDB. The dataset is specifically designed to explore graphical humor by emphasizing subtle visual cues that trigger humor and mitigating potential biases. It enables evaluation through binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?), capturing the subjective nature of humor perception . The study aims to address the challenge of understanding complex scenes, particularly those involving humor, by providing a curated dataset that can be used to deepen the understanding of abstract concepts such as humor .


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

The paper "Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor" introduces several novel ideas, methods, and models to advance visual humor understanding . Here are some key proposals from the paper:

  1. HumorDB Dataset: The paper introduces the HumorDB dataset, which is an image-only dataset meticulously curated to enhance visual humor understanding. This dataset consists of image pairs with varying humor ratings, emphasizing subtle visual cues that trigger humor and mitigating potential biases. It enables evaluation through binary classification, range regression, and pairwise comparison tasks, capturing the subjective nature of humor perception .

  2. Evaluation Tasks: The dataset facilitates various evaluation tasks such as binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?). These tasks aim to assess the ability of models to understand graphical humor and interpret abstract concepts like humor in images .

  3. Vision-Language Models: The paper highlights the effectiveness of vision-language models, particularly those leveraging large language models, in understanding graphical humor. These models, such as LLaVA, GPT-4, and Gemini-Flash, outperform vision-only models like ViT_Huge, SwinV2_Large, and DinoV2_Large. Additionally, models trained with supporting words for images further enhance performance, showcasing the importance of multimodal approaches in humor understanding .

  4. Zero-Shot Evaluations: The study includes zero-shot evaluations using models like GPT-4o and Gemini-Flash, demonstrating promising results even without explicit training on the dataset. These models show adequate performance in tasks like range regression, approaching the consistency of human responses. This highlights the potential of HumorDB as a valuable benchmark for assessing powerful vision-language models on challenging tasks .

  5. Training Details: The paper provides insights into the training details of the models used for evaluating humor understanding. Models were trained using the Adam optimization algorithm with weight decay, and a hyperparameter grid search was conducted to optimize performance. Different loss functions were used for binary classification and regression tasks. The training process involved fine-tuning models and ensuring statistical robustness through multiple experiment runs .

In summary, the paper proposes the HumorDB dataset, emphasizes the effectiveness of vision-language models in humor understanding, showcases zero-shot evaluations, and provides detailed training methodologies to advance research in graphical humor perception . The paper "Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor" introduces several characteristics and advantages compared to previous methods, as detailed in the paper :

  1. HumorDB Dataset: The HumorDB dataset is a novel image-only dataset designed to advance visual humor understanding. It consists of meticulously curated image pairs with contrasting humor ratings, emphasizing subtle visual cues that trigger humor. This dataset enables evaluation through binary classification, range regression, and pairwise comparison tasks, capturing the subjective nature of humor perception .

  2. Evaluation Tasks: The dataset facilitates various evaluation tasks such as binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?). These tasks allow for a comprehensive assessment of models' ability to understand graphical humor and interpret abstract concepts like humor in images .

  3. Vision-Language Models: The study highlights the effectiveness of vision-language models, particularly those leveraging large language models, in understanding graphical humor. Vision-language models like LLaVA, GPT-4, and Gemini-Flash outperform vision-only models such as ViT_Huge, SwinV2_Large, and DinoV2_Large. Models trained with supporting words for images further enhance performance, emphasizing the importance of multimodal approaches in humor understanding .

  4. Zero-Shot Evaluations: The paper includes zero-shot evaluations using models like GPT-4o and Gemini-Flash, showcasing promising results even without explicit training on the dataset. These models demonstrate adequate performance in tasks like range regression, approaching the consistency of human responses. This highlights the potential of HumorDB as a valuable benchmark for assessing powerful vision-language models on challenging tasks .

  5. Training Details: The models in the study were trained using the Adam optimization algorithm with weight decay. A hyperparameter grid search was conducted to optimize performance, and models were fine-tuned to ensure statistical robustness. The training process involved multiple experiment runs and utilized various resources like Nvidia GPUs for training .

  6. Human Performance Comparison: The models evaluated in the study exhibited lower performance compared to humans in the Binary task, despite being fine-tuned on average ratings. However, for the Range task, the models reached comparable performance to humans. In the Comparison task, the models performed better than chance but worse than humans, highlighting the challenges in comparing human and machine algorithms in vision/language tasks .

In summary, the characteristics and advantages of the HumorDB dataset and the methodologies employed in the study demonstrate advancements in visual humor understanding, the effectiveness of vision-language models, and the potential for zero-shot evaluations in assessing powerful multimodal models on challenging tasks.


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 studies exist in the field of graphical humor understanding. Noteworthy researchers in this area include Vedaant Jain from the University of Illinois Urbana-Champaign, Felipe dos Santos Alves Feitosa from the University of São Paulo, and Gabriel Kreiman from Children’s Hospital, Harvard Medical School . Other researchers who have contributed to this field include S. Abnar, W. Zuidema, S. Attardo, D. Bertero, P. Fung, D. S. Chauhan, L. Chen, C. M. Lee, D. Li, J. Li, R. Li, P. P. Liang, H. Liu, Z. Liu, R. Courant, V. Kalogeiton, among others .

The key to the solution mentioned in the paper is the development of HumorDB, a novel image-only dataset designed to advance visual humor understanding. This dataset consists of meticulously curated image pairs with contrasting humor ratings, focusing on subtle visual cues that trigger humor and mitigating potential biases. HumorDB enables evaluation through binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?), effectively capturing the subjective nature of humor perception. The paper highlights that while vision-only models face challenges, vision-language models, especially those leveraging large language models, show promising results. The dataset and code are open-sourced under the CC BY 4.0 license .


How were the experiments in the paper designed?

The experiments in the paper were designed by evaluating state-of-the-art visual architectures, including vision-only and vision-language models, using both pretrained and trained-from-scratch settings . The models were trained with a hyperparameter grid search across learning rates, batch sizes, and weight decay parameters . The experiments included tasks such as binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?) . These tasks aimed to capture the subjective nature of humor perception and involved training models with pretrained weights and fine-tuning or training with random initialization of weights . Additionally, attention maps were examined using the attention rollout technique to understand how models classified images and identified humor triggers .


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

The dataset used for quantitative evaluation in the study is called HumorDB. It is a curated dataset and benchmark specifically designed to investigate graphical humor . The code for the dataset is open source and available on the Github repository at the following link: https://github.com/kreimanlab/HumorDB/ .


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 needed verification. The study involved evaluating various state-of-the-art visual architectures, including vision-only and vision-language models, both pretrained and trained-from-scratch, across different tasks such as Binary classification, Range regression, and Comparison tasks . The models exhibited performance levels that were comparable to human assessments in the Range task, indicating a good alignment with human judgments . Additionally, the study highlighted the importance of controls in computer vision to avoid biases that could impact classification accuracy .

The research employed a meticulous approach in building the HumorDB dataset, which included gathering diverse images from multiple sources and creating modified image pairs to elicit contrasting humor responses . This critical control measure aimed to ensure that models understand humor nuances rather than relying on spurious correlations, thus addressing the challenge of potential biases in online images . The dataset creation process involved human evaluations through online psychophysics experiments with a significant number of participants, ensuring a robust evaluation of the curated image sets .

Furthermore, the study's results demonstrated that vision-language models, particularly those leveraging large language models, showed promising performance in understanding graphical humor . The models' ability to perform above chance levels in the Comparison task, although not as well as humans, indicates progress in capturing the subjective nature of humor perception . The research findings underscore the complexity of scene understanding, particularly in assessing graphical humor, and the need for models to effectively reason about abstract concepts .

In conclusion, the experiments and results presented in the paper offer strong support for the scientific hypotheses under investigation by showcasing the performance of various models in understanding graphical humor, highlighting the importance of controls in dataset creation, and emphasizing the challenges and advancements in scene interpretation within the realm of computer vision .


What are the contributions of this paper?

The paper "Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor" makes several contributions:

  • Introduces HumorDB, an image-only dataset designed to advance visual humor understanding by emphasizing subtle visual cues that trigger humor and mitigating potential biases .
  • Enables evaluation through binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?), capturing the subjective nature of humor perception .
  • Shows that while vision-only models struggle with humor understanding, vision-language models, especially those leveraging large language models, demonstrate promising results .
  • Provides potential as a valuable zero-shot benchmark for powerful large multimodal models .
  • Open-sources both the dataset and code under the CC BY 4.0 license .

What work can be continued in depth?

Further research in the field of humor understanding and AI can be expanded in several directions based on the existing dataset and benchmarks:

  • Deepening Scene Understanding: Continued efforts can focus on advancing the understanding of complex scenes involving humor, which remains a significant challenge in computer vision .
  • Enhancing Humor Perception: Research can aim to develop AI systems that truly capture human humor, which could have various beneficial applications such as entertainment, therapy, and improving our understanding of human cognition .
  • Mitigating Risks: There is a need to address the potential misuse of humor by AI systems, which could lead to offensive outputs or unintentionally perpetuate stereotypes and biases. Improving models' understanding of abstract concepts like humor may also contribute to enhanced content moderation systems .
  • Exploring Multimodal Models: Further exploration can be done on vision-language models, particularly those leveraging large language models, as they have shown promising results in humor understanding tasks .
  • Zero-Shot Evaluation: The dataset HumorDB provides a valuable benchmark for zero-shot evaluation of large multimodal models due to its inclusion of modified images, which present a challenge not encountered during the training of these models, offering a more realistic assessment of their generalization abilities .

Tables

2

Introduction
Background
Emergence of humor in computer vision research
Challenges for traditional vision-only models
Objective
To investigate visual humor understanding in AI systems
Evaluate the impact of vision-language models and multimodal inputs
Method
Data Collection
Image pair selection: 3,545 diverse and humor-rated examples
Context and subtlety: Purposeful variety in humor types
Data Preprocessing
Humor ratings: Annotation process and rating scale
Pairing methodology: Ensuring balanced and diverse samples
Model Evaluation
Binary classification: Identifying humorous vs. non-humorous images
Humor ranking: Assessing models' ability to rank humor levels
Image comparison: Evaluating model comprehension of humor differences
Human Evaluation
550 participants: Reliability and originality in humor perception
Ground truth validation: Human judgment as benchmark
Multimodal Inputs and Pretraining
Impact of LLaVA and GPT-4: Performance comparison
Role of multimodal fusion in humor understanding
Dataset Characteristics
CC BY 4.0 license: Accessibility and attribution requirements
Applications: Content moderation and abstract concept comprehension
Conclusion
Benchmark for humor detection in AI
Future research directions in humor understanding and AI systems
Implications for real-world applications and ethical considerations
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What is the purpose of the HumorDB dataset in computer vision research?
What is HumorDB?
How do traditional vision-only models compare to vision-language models in understanding humor?
What tasks does the study evaluate models on using the HumorDB dataset?

Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor

Veedant Jain, Felipe dos Santos Alves Feitosa, Gabriel Kreiman·June 19, 2024

Summary

HumorDB is a novel image dataset introduced in the paper to investigate visual humor understanding in computer vision. The dataset consists of 3,545 image pairs with varying humor ratings, designed to challenge models in understanding subtle humor and context. Traditional vision-only models struggle, while vision-language models, particularly those incorporating large language models like LLaVA and GPT-4, exhibit better performance. The study evaluates models on tasks such as binary classification, humor ranking, and image comparison, with a focus on pretraining and the role of multimodal inputs. Human evaluations, involving 550 participants, ensure dataset reliability and highlight the importance of originality in humor perception. The dataset, released under CC BY 4.0, serves as a benchmark for humor detection and abstract concept comprehension in AI systems, with implications for content moderation and future research in the field.
Mind map
Role of multimodal fusion in humor understanding
Impact of LLaVA and GPT-4: Performance comparison
Image comparison: Evaluating model comprehension of humor differences
Humor ranking: Assessing models' ability to rank humor levels
Binary classification: Identifying humorous vs. non-humorous images
Applications: Content moderation and abstract concept comprehension
CC BY 4.0 license: Accessibility and attribution requirements
Multimodal Inputs and Pretraining
Model Evaluation
Context and subtlety: Purposeful variety in humor types
Image pair selection: 3,545 diverse and humor-rated examples
Evaluate the impact of vision-language models and multimodal inputs
To investigate visual humor understanding in AI systems
Challenges for traditional vision-only models
Emergence of humor in computer vision research
Implications for real-world applications and ethical considerations
Future research directions in humor understanding and AI systems
Benchmark for humor detection in AI
Dataset Characteristics
Human Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Method
Introduction
Outline
Introduction
Background
Emergence of humor in computer vision research
Challenges for traditional vision-only models
Objective
To investigate visual humor understanding in AI systems
Evaluate the impact of vision-language models and multimodal inputs
Method
Data Collection
Image pair selection: 3,545 diverse and humor-rated examples
Context and subtlety: Purposeful variety in humor types
Data Preprocessing
Humor ratings: Annotation process and rating scale
Pairing methodology: Ensuring balanced and diverse samples
Model Evaluation
Binary classification: Identifying humorous vs. non-humorous images
Humor ranking: Assessing models' ability to rank humor levels
Image comparison: Evaluating model comprehension of humor differences
Human Evaluation
550 participants: Reliability and originality in humor perception
Ground truth validation: Human judgment as benchmark
Multimodal Inputs and Pretraining
Impact of LLaVA and GPT-4: Performance comparison
Role of multimodal fusion in humor understanding
Dataset Characteristics
CC BY 4.0 license: Accessibility and attribution requirements
Applications: Content moderation and abstract concept comprehension
Conclusion
Benchmark for humor detection in AI
Future research directions in humor understanding and AI systems
Implications for real-world applications and ethical considerations
Key findings
12

Paper digest

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

The paper "Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor" aims to address the challenge of understanding complex scenes involving humor, specifically in graphical form, which remains a significant challenge despite advancements in computer vision . This paper introduces HumorDB, a novel image-only dataset designed to advance visual humor understanding by emphasizing subtle visual cues that trigger humor and mitigating potential biases . While there have been emerging datasets and approaches for humor understanding in multi-modal contexts, such as videos, there is a lack of datasets focusing solely on images, which are crucial for developing visually intelligent systems in the future . Therefore, the paper's focus on humor understanding through images presents a new and important problem in the field of computer vision and humor perception .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to advancing visual humor understanding through the creation of a novel image-only dataset called HumorDB. The dataset is specifically designed to explore graphical humor by emphasizing subtle visual cues that trigger humor and mitigating potential biases. It enables evaluation through binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?), capturing the subjective nature of humor perception . The study aims to address the challenge of understanding complex scenes, particularly those involving humor, by providing a curated dataset that can be used to deepen the understanding of abstract concepts such as humor .


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

The paper "Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor" introduces several novel ideas, methods, and models to advance visual humor understanding . Here are some key proposals from the paper:

  1. HumorDB Dataset: The paper introduces the HumorDB dataset, which is an image-only dataset meticulously curated to enhance visual humor understanding. This dataset consists of image pairs with varying humor ratings, emphasizing subtle visual cues that trigger humor and mitigating potential biases. It enables evaluation through binary classification, range regression, and pairwise comparison tasks, capturing the subjective nature of humor perception .

  2. Evaluation Tasks: The dataset facilitates various evaluation tasks such as binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?). These tasks aim to assess the ability of models to understand graphical humor and interpret abstract concepts like humor in images .

  3. Vision-Language Models: The paper highlights the effectiveness of vision-language models, particularly those leveraging large language models, in understanding graphical humor. These models, such as LLaVA, GPT-4, and Gemini-Flash, outperform vision-only models like ViT_Huge, SwinV2_Large, and DinoV2_Large. Additionally, models trained with supporting words for images further enhance performance, showcasing the importance of multimodal approaches in humor understanding .

  4. Zero-Shot Evaluations: The study includes zero-shot evaluations using models like GPT-4o and Gemini-Flash, demonstrating promising results even without explicit training on the dataset. These models show adequate performance in tasks like range regression, approaching the consistency of human responses. This highlights the potential of HumorDB as a valuable benchmark for assessing powerful vision-language models on challenging tasks .

  5. Training Details: The paper provides insights into the training details of the models used for evaluating humor understanding. Models were trained using the Adam optimization algorithm with weight decay, and a hyperparameter grid search was conducted to optimize performance. Different loss functions were used for binary classification and regression tasks. The training process involved fine-tuning models and ensuring statistical robustness through multiple experiment runs .

In summary, the paper proposes the HumorDB dataset, emphasizes the effectiveness of vision-language models in humor understanding, showcases zero-shot evaluations, and provides detailed training methodologies to advance research in graphical humor perception . The paper "Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor" introduces several characteristics and advantages compared to previous methods, as detailed in the paper :

  1. HumorDB Dataset: The HumorDB dataset is a novel image-only dataset designed to advance visual humor understanding. It consists of meticulously curated image pairs with contrasting humor ratings, emphasizing subtle visual cues that trigger humor. This dataset enables evaluation through binary classification, range regression, and pairwise comparison tasks, capturing the subjective nature of humor perception .

  2. Evaluation Tasks: The dataset facilitates various evaluation tasks such as binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?). These tasks allow for a comprehensive assessment of models' ability to understand graphical humor and interpret abstract concepts like humor in images .

  3. Vision-Language Models: The study highlights the effectiveness of vision-language models, particularly those leveraging large language models, in understanding graphical humor. Vision-language models like LLaVA, GPT-4, and Gemini-Flash outperform vision-only models such as ViT_Huge, SwinV2_Large, and DinoV2_Large. Models trained with supporting words for images further enhance performance, emphasizing the importance of multimodal approaches in humor understanding .

  4. Zero-Shot Evaluations: The paper includes zero-shot evaluations using models like GPT-4o and Gemini-Flash, showcasing promising results even without explicit training on the dataset. These models demonstrate adequate performance in tasks like range regression, approaching the consistency of human responses. This highlights the potential of HumorDB as a valuable benchmark for assessing powerful vision-language models on challenging tasks .

  5. Training Details: The models in the study were trained using the Adam optimization algorithm with weight decay. A hyperparameter grid search was conducted to optimize performance, and models were fine-tuned to ensure statistical robustness. The training process involved multiple experiment runs and utilized various resources like Nvidia GPUs for training .

  6. Human Performance Comparison: The models evaluated in the study exhibited lower performance compared to humans in the Binary task, despite being fine-tuned on average ratings. However, for the Range task, the models reached comparable performance to humans. In the Comparison task, the models performed better than chance but worse than humans, highlighting the challenges in comparing human and machine algorithms in vision/language tasks .

In summary, the characteristics and advantages of the HumorDB dataset and the methodologies employed in the study demonstrate advancements in visual humor understanding, the effectiveness of vision-language models, and the potential for zero-shot evaluations in assessing powerful multimodal models on challenging tasks.


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 studies exist in the field of graphical humor understanding. Noteworthy researchers in this area include Vedaant Jain from the University of Illinois Urbana-Champaign, Felipe dos Santos Alves Feitosa from the University of São Paulo, and Gabriel Kreiman from Children’s Hospital, Harvard Medical School . Other researchers who have contributed to this field include S. Abnar, W. Zuidema, S. Attardo, D. Bertero, P. Fung, D. S. Chauhan, L. Chen, C. M. Lee, D. Li, J. Li, R. Li, P. P. Liang, H. Liu, Z. Liu, R. Courant, V. Kalogeiton, among others .

The key to the solution mentioned in the paper is the development of HumorDB, a novel image-only dataset designed to advance visual humor understanding. This dataset consists of meticulously curated image pairs with contrasting humor ratings, focusing on subtle visual cues that trigger humor and mitigating potential biases. HumorDB enables evaluation through binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?), effectively capturing the subjective nature of humor perception. The paper highlights that while vision-only models face challenges, vision-language models, especially those leveraging large language models, show promising results. The dataset and code are open-sourced under the CC BY 4.0 license .


How were the experiments in the paper designed?

The experiments in the paper were designed by evaluating state-of-the-art visual architectures, including vision-only and vision-language models, using both pretrained and trained-from-scratch settings . The models were trained with a hyperparameter grid search across learning rates, batch sizes, and weight decay parameters . The experiments included tasks such as binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?) . These tasks aimed to capture the subjective nature of humor perception and involved training models with pretrained weights and fine-tuning or training with random initialization of weights . Additionally, attention maps were examined using the attention rollout technique to understand how models classified images and identified humor triggers .


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

The dataset used for quantitative evaluation in the study is called HumorDB. It is a curated dataset and benchmark specifically designed to investigate graphical humor . The code for the dataset is open source and available on the Github repository at the following link: https://github.com/kreimanlab/HumorDB/ .


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 needed verification. The study involved evaluating various state-of-the-art visual architectures, including vision-only and vision-language models, both pretrained and trained-from-scratch, across different tasks such as Binary classification, Range regression, and Comparison tasks . The models exhibited performance levels that were comparable to human assessments in the Range task, indicating a good alignment with human judgments . Additionally, the study highlighted the importance of controls in computer vision to avoid biases that could impact classification accuracy .

The research employed a meticulous approach in building the HumorDB dataset, which included gathering diverse images from multiple sources and creating modified image pairs to elicit contrasting humor responses . This critical control measure aimed to ensure that models understand humor nuances rather than relying on spurious correlations, thus addressing the challenge of potential biases in online images . The dataset creation process involved human evaluations through online psychophysics experiments with a significant number of participants, ensuring a robust evaluation of the curated image sets .

Furthermore, the study's results demonstrated that vision-language models, particularly those leveraging large language models, showed promising performance in understanding graphical humor . The models' ability to perform above chance levels in the Comparison task, although not as well as humans, indicates progress in capturing the subjective nature of humor perception . The research findings underscore the complexity of scene understanding, particularly in assessing graphical humor, and the need for models to effectively reason about abstract concepts .

In conclusion, the experiments and results presented in the paper offer strong support for the scientific hypotheses under investigation by showcasing the performance of various models in understanding graphical humor, highlighting the importance of controls in dataset creation, and emphasizing the challenges and advancements in scene interpretation within the realm of computer vision .


What are the contributions of this paper?

The paper "Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor" makes several contributions:

  • Introduces HumorDB, an image-only dataset designed to advance visual humor understanding by emphasizing subtle visual cues that trigger humor and mitigating potential biases .
  • Enables evaluation through binary classification (Funny or Not Funny), range regression (funniness on a scale from 1 to 10), and pairwise comparison tasks (Which Image is Funnier?), capturing the subjective nature of humor perception .
  • Shows that while vision-only models struggle with humor understanding, vision-language models, especially those leveraging large language models, demonstrate promising results .
  • Provides potential as a valuable zero-shot benchmark for powerful large multimodal models .
  • Open-sources both the dataset and code under the CC BY 4.0 license .

What work can be continued in depth?

Further research in the field of humor understanding and AI can be expanded in several directions based on the existing dataset and benchmarks:

  • Deepening Scene Understanding: Continued efforts can focus on advancing the understanding of complex scenes involving humor, which remains a significant challenge in computer vision .
  • Enhancing Humor Perception: Research can aim to develop AI systems that truly capture human humor, which could have various beneficial applications such as entertainment, therapy, and improving our understanding of human cognition .
  • Mitigating Risks: There is a need to address the potential misuse of humor by AI systems, which could lead to offensive outputs or unintentionally perpetuate stereotypes and biases. Improving models' understanding of abstract concepts like humor may also contribute to enhanced content moderation systems .
  • Exploring Multimodal Models: Further exploration can be done on vision-language models, particularly those leveraging large language models, as they have shown promising results in humor understanding tasks .
  • Zero-Shot Evaluation: The dataset HumorDB provides a valuable benchmark for zero-shot evaluation of large multimodal models due to its inclusion of modified images, which present a challenge not encountered during the training of these models, offering a more realistic assessment of their generalization abilities .
Tables
2
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.