Evaluating Vision-Language Models on Bistable Images

Artemis Panagopoulou, Coby Melkin, Chris Callison-Burch·May 29, 2024

Summary

This study investigates vision-language models' performance on bistable images, which present multiple interpretations. Researchers analyzed 12 models from diverse architectures, including CLIP, BLIP, and LLaVA, through a large dataset of 29 bistable images and manipulations. The findings show that models tend to favor one interpretation, with limited variance except for rotation, and differ from human biases. Language prompts and synonymous labels significantly impact model interpretations, suggesting that language priors play a crucial role in understanding these images. The study highlights the need to consider the influence of training data and the discrepancy between model and human perception in the context of bistable image comprehension.

Key findings

12

Paper digest

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

The paper aims to evaluate vision-language models (VLMs) on bistable images to understand how these models interpret ambiguous visual stimuli and compare their interpretations with human perceptions . This evaluation focuses on the discrepancies between VLM interpretations and human perceptions of bistable images, highlighting the limited correspondence between the two . The study also replicates a previous human study by Takashima et al. (2012) to assess VLM-human alignment in processing bistable images . While the evaluation of VLMs on bistable images is not a new problem, this paper contributes to the understanding of how these models process ambiguous visual stimuli and their alignment with human interpretations .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate scientific hypotheses related to bistable images and their interpretation by Vision-Language Models (VLMs) . The study explores the impact of synonymous interpretation labels, prompt variation, and image manipulations on VLMs' perception of bistable images . Additionally, it investigates the sensitivity of VLMs to rotation, brightness, and color variations in image interpretation, highlighting the divergence between VLM processing and human perception of bistable images . The research delves into model-specific trends, probability distributions, and variations in interpretations across different models, shedding light on the underlying mechanisms influencing VLMs' image processing .


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

The paper proposes novel ideas, methods, and models in the evaluation of Vision-Language Models (VLMs) on bistable images. The study utilized six VLM families with a total of twelve different models for classification and generation tasks . The models used in the evaluation included CLIP, Idefics 9b, LLaVA1.5, mPLUG-Owl, InstructBLIP, and BLIP-2 . These models were queried with default generation parameters and prompts suggested by their respective model pages on Huggingface .

In the experimental setup, the paper adapted the outputs of the VLMs to simulate classification using a loss ranking technique for classification tasks, determining the negative log likelihood of each candidate label . For the generative setup, the models were prompted to describe the images following the format recommended in the HuggingFace documentation for captioning .

The study explored the influence of visual modifications on perception by creating 116 variations for each image through controlled manipulations, including adjustments to image brightness, application of color tints, and image rotations . These manipulations aimed to investigate how VLMs process and interpret bistable images under different visual conditions.

Furthermore, the paper introduced a dataset comprising 29 bistable images categorized into seven distinct types, sourced from online platforms and academic studies . The dataset included classic categories of bistable illusions such as the Rubin Vase, Necker Cube, Duck-Rabbit, and Young-Old Woman, each with several iconic versions of the respective illusion type .

Overall, the paper contributes to the field by providing a comprehensive evaluation of VLMs on bistable images, introducing new methodologies for classification and generation tasks, and highlighting the sensitivity of VLMs to visual modifications and illusions. The paper introduces novel characteristics and advantages in evaluating Vision-Language Models (VLMs) on bistable images compared to previous methods. One key aspect is the utilization of six VLM families with a total of twelve different models for classification and generation tasks, including CLIP, Idefics 9b, LLaVA1.5, mPLUG-Owl, InstructBLIP, and BLIP-2 . These models were queried with default generation parameters and prompts suggested by their respective model pages on Huggingface .

In terms of experimental setup, the paper innovatively adapted the outputs of VLMs to simulate classification using a loss ranking technique for classification tasks, determining the negative log likelihood of each candidate label . For generative tasks, the models were prompted to describe images following specific formats recommended in the HuggingFace documentation for captioning .

Furthermore, the study explored the impact of visual modifications on perception by creating 116 variations for each image through controlled manipulations such as adjustments to image brightness, color tints, and rotations . These manipulations aimed to investigate how VLMs process and interpret bistable images under different visual conditions.

Moreover, the paper introduced a dataset comprising 29 bistable images categorized into seven distinct types, sourced from online platforms and academic studies . This dataset included classic categories of bistable illusions like the Rubin Vase, Necker Cube, Duck-Rabbit, and Young-Old Woman, each with several iconic versions of the respective illusion type .

Overall, the paper's innovative approach lies in its comprehensive evaluation of VLMs on bistable images, introducing new methodologies for classification and generation tasks, and highlighting the sensitivity of VLMs to visual modifications and illusions. The study's detailed analysis and experimental setup provide valuable insights into the interaction between VLMs and cognitive illusions, offering a significant advancement in this research domain .


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 vision-language models and bistable images. Noteworthy researchers in this area include Jürgen Kornmeier, Michael Bach, Ranjay Krishna, Yuke Zhu, Oliver Groth, and many others . These researchers have contributed to various aspects of connecting language and vision, studying perceptual bias, and exploring the interaction between visual perception and language understanding.

The key to the solution mentioned in the paper "Evaluating Vision-Language Models on Bistable Images" involves utilizing a dataset comprising 29 bistable images categorized into seven distinct types, sourced from online platforms and academic studies. These images include classic categories of bistable illusions like the Rubin Vase, Necker Cube, Duck-Rabbit, and Young-Old Woman. The study explores the influence of visual modifications on perception by creating 116 variations for each image through controlled manipulations such as adjustments to image brightness, application of color tints, and image rotations . The experimental setup involved evaluating six Vision-Language Model families to assess the models' performance on these bistable images .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate Vision-Language Models (VLMs) on bistable images. The study conducted an extensive examination of VLMs using a dataset of 29 bistable images, along with their associated labels, subjected to 116 different manipulations in brightness, tint, and rotation . The dataset comprised seven distinct types of bistable images, sourced from online platforms and academic studies, including classic categories like the Rubin Vase, Necker Cube, Duck-Rabbit, and Young-Old Woman . To explore the influence of visual modifications on perception, the study created variations for each image through controlled manipulations such as adjustments in brightness, application of color tints (red, green, blue, yellow, magenta, cyan), and image rotations from 0 to 360 degrees every 10 degrees .


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

The dataset used for quantitative evaluation in the study is called "Obelics: An open web-scale filtered dataset of interleaved image-text documents" . The code for this dataset 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 "Evaluating Vision-Language Models on Bistable Images" offer substantial support for the scientific hypotheses that require verification. The study conducted a comprehensive examination of vision-language models using bistable images, which are visual stimuli that can be perceived in two distinct interpretations . The researchers manually curated a dataset of 29 bistable images and subjected them to various manipulations in brightness, tint, and rotation, evaluating twelve different models across six model architectures .

The findings from the study revealed that, with a few exceptions, there was a clear preference among the models for one interpretation over another, indicating minimal variance under image manipulations . This suggests that the models exhibited consistent behavior in their interpretations of the bistable images. Additionally, the comparison between the models' preferences and human interpretations highlighted differences, showing that the models do not always align with human biases and initial perceptions .

Moreover, the research delved into the influence of variations in prompts and the use of synonymous labels on model interpretations. The study discovered that these factors significantly impacted model interpretations more than image manipulations, emphasizing the substantial influence of language priors on bistable image interpretations compared to image-text training data . This analysis underscores the importance of considering linguistic cues in understanding how vision-language models interpret ambiguous visual stimuli.

In conclusion, the experiments and results presented in the paper provide robust support for the scientific hypotheses that needed verification by offering insights into the behavior of vision-language models when presented with bistable images and highlighting the impact of language priors on their interpretations .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Architectural Differences: The study summarizes the architectural variances of the models used, as detailed in Table 1 of the paper .
  • Dataset Information: It lists the datasets used for pre-training and instruction tuning in Table 2 of the paper .
  • Research Support: The research was supported by a gift from AWS AI for research in Trustworthy AI, emphasizing the importance of trustworthy AI in the study .

What work can be continued in depth?

Further research in this area can delve deeper into several aspects:

  • Exploring the impact of training data: Investigating how different training datasets influence the preferences and interpretations of vision-language models when presented with ambiguous images .
  • Analyzing the role of language model priors: Understanding how the base language models (LLMs) used during training affect the interpretation of bistable images by vision-language models, highlighting the significance of LLM priors in processing visual ambiguity .
  • Investigating the effect of textual modifications: Studying how variations in prompts and the use of synonymous labels impact model interpretations of ambiguous images, emphasizing the importance of language model priors in guiding vision-language models' responses .
  • Comparing with traditional vision models: Contrasting the handling of visual ambiguity by vision-language models with traditional convolutional neural networks (CNNs) that focus on geometric optical illusions, showcasing the differences in biases and interpretations influenced by language model priors .

Introduction
Background
Overview of bistable images and their ambiguity
Importance of understanding model perception in ambiguous stimuli
Objective
To assess the performance of vision-language models on bistable images
To explore the role of language priors and model biases
Method
Data Collection
Selection of 12 diverse models (e.g., CLIP, BLIP, LLaVA)
Development of a large dataset: 29 bistable images and manipulations
Inclusion of various interpretations for each image
Data Preprocessing
Standardization of image and text inputs for model evaluation
Creation of different language prompts and synonymous labels
Model Analysis
Performance metrics: accuracy, consistency, and variance across interpretations
Comparison with human interpretation patterns
Influence of Language
Experimentation with prompts: controlled and open-ended
Analysis of the impact on model interpretation variability
Human Bias Comparison
Collection of human interpretations for the same images
Quantifying and comparing model-human biases
Results
Model interpretation patterns: favored interpretations and consistency
Effect of rotation on model variance
Significance of language priors in model comprehension
Discussion
Discrepancy between model and human perception
The role of training data in shaping model understanding
Limitations and implications for future research
Conclusion
Summary of findings and their implications for vision-language model development
Recommendations for improving model performance on bistable images
Open questions and directions for future work in this area
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
How do language prompts and synonymous labels affect model interpretations?
What type of images does the study focus on?
Which models were analyzed in the investigation?
What conclusion does the study draw regarding the role of language priors in bistable image comprehension?

Evaluating Vision-Language Models on Bistable Images

Artemis Panagopoulou, Coby Melkin, Chris Callison-Burch·May 29, 2024

Summary

This study investigates vision-language models' performance on bistable images, which present multiple interpretations. Researchers analyzed 12 models from diverse architectures, including CLIP, BLIP, and LLaVA, through a large dataset of 29 bistable images and manipulations. The findings show that models tend to favor one interpretation, with limited variance except for rotation, and differ from human biases. Language prompts and synonymous labels significantly impact model interpretations, suggesting that language priors play a crucial role in understanding these images. The study highlights the need to consider the influence of training data and the discrepancy between model and human perception in the context of bistable image comprehension.
Mind map
Analysis of the impact on model interpretation variability
Experimentation with prompts: controlled and open-ended
Comparison with human interpretation patterns
Performance metrics: accuracy, consistency, and variance across interpretations
Quantifying and comparing model-human biases
Collection of human interpretations for the same images
Influence of Language
Model Analysis
Inclusion of various interpretations for each image
Development of a large dataset: 29 bistable images and manipulations
Selection of 12 diverse models (e.g., CLIP, BLIP, LLaVA)
To explore the role of language priors and model biases
To assess the performance of vision-language models on bistable images
Importance of understanding model perception in ambiguous stimuli
Overview of bistable images and their ambiguity
Open questions and directions for future work in this area
Recommendations for improving model performance on bistable images
Summary of findings and their implications for vision-language model development
Limitations and implications for future research
The role of training data in shaping model understanding
Discrepancy between model and human perception
Significance of language priors in model comprehension
Effect of rotation on model variance
Model interpretation patterns: favored interpretations and consistency
Human Bias Comparison
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Results
Method
Introduction
Outline
Introduction
Background
Overview of bistable images and their ambiguity
Importance of understanding model perception in ambiguous stimuli
Objective
To assess the performance of vision-language models on bistable images
To explore the role of language priors and model biases
Method
Data Collection
Selection of 12 diverse models (e.g., CLIP, BLIP, LLaVA)
Development of a large dataset: 29 bistable images and manipulations
Inclusion of various interpretations for each image
Data Preprocessing
Standardization of image and text inputs for model evaluation
Creation of different language prompts and synonymous labels
Model Analysis
Performance metrics: accuracy, consistency, and variance across interpretations
Comparison with human interpretation patterns
Influence of Language
Experimentation with prompts: controlled and open-ended
Analysis of the impact on model interpretation variability
Human Bias Comparison
Collection of human interpretations for the same images
Quantifying and comparing model-human biases
Results
Model interpretation patterns: favored interpretations and consistency
Effect of rotation on model variance
Significance of language priors in model comprehension
Discussion
Discrepancy between model and human perception
The role of training data in shaping model understanding
Limitations and implications for future research
Conclusion
Summary of findings and their implications for vision-language model development
Recommendations for improving model performance on bistable images
Open questions and directions for future work in this area
Key findings
12

Paper digest

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

The paper aims to evaluate vision-language models (VLMs) on bistable images to understand how these models interpret ambiguous visual stimuli and compare their interpretations with human perceptions . This evaluation focuses on the discrepancies between VLM interpretations and human perceptions of bistable images, highlighting the limited correspondence between the two . The study also replicates a previous human study by Takashima et al. (2012) to assess VLM-human alignment in processing bistable images . While the evaluation of VLMs on bistable images is not a new problem, this paper contributes to the understanding of how these models process ambiguous visual stimuli and their alignment with human interpretations .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate scientific hypotheses related to bistable images and their interpretation by Vision-Language Models (VLMs) . The study explores the impact of synonymous interpretation labels, prompt variation, and image manipulations on VLMs' perception of bistable images . Additionally, it investigates the sensitivity of VLMs to rotation, brightness, and color variations in image interpretation, highlighting the divergence between VLM processing and human perception of bistable images . The research delves into model-specific trends, probability distributions, and variations in interpretations across different models, shedding light on the underlying mechanisms influencing VLMs' image processing .


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

The paper proposes novel ideas, methods, and models in the evaluation of Vision-Language Models (VLMs) on bistable images. The study utilized six VLM families with a total of twelve different models for classification and generation tasks . The models used in the evaluation included CLIP, Idefics 9b, LLaVA1.5, mPLUG-Owl, InstructBLIP, and BLIP-2 . These models were queried with default generation parameters and prompts suggested by their respective model pages on Huggingface .

In the experimental setup, the paper adapted the outputs of the VLMs to simulate classification using a loss ranking technique for classification tasks, determining the negative log likelihood of each candidate label . For the generative setup, the models were prompted to describe the images following the format recommended in the HuggingFace documentation for captioning .

The study explored the influence of visual modifications on perception by creating 116 variations for each image through controlled manipulations, including adjustments to image brightness, application of color tints, and image rotations . These manipulations aimed to investigate how VLMs process and interpret bistable images under different visual conditions.

Furthermore, the paper introduced a dataset comprising 29 bistable images categorized into seven distinct types, sourced from online platforms and academic studies . The dataset included classic categories of bistable illusions such as the Rubin Vase, Necker Cube, Duck-Rabbit, and Young-Old Woman, each with several iconic versions of the respective illusion type .

Overall, the paper contributes to the field by providing a comprehensive evaluation of VLMs on bistable images, introducing new methodologies for classification and generation tasks, and highlighting the sensitivity of VLMs to visual modifications and illusions. The paper introduces novel characteristics and advantages in evaluating Vision-Language Models (VLMs) on bistable images compared to previous methods. One key aspect is the utilization of six VLM families with a total of twelve different models for classification and generation tasks, including CLIP, Idefics 9b, LLaVA1.5, mPLUG-Owl, InstructBLIP, and BLIP-2 . These models were queried with default generation parameters and prompts suggested by their respective model pages on Huggingface .

In terms of experimental setup, the paper innovatively adapted the outputs of VLMs to simulate classification using a loss ranking technique for classification tasks, determining the negative log likelihood of each candidate label . For generative tasks, the models were prompted to describe images following specific formats recommended in the HuggingFace documentation for captioning .

Furthermore, the study explored the impact of visual modifications on perception by creating 116 variations for each image through controlled manipulations such as adjustments to image brightness, color tints, and rotations . These manipulations aimed to investigate how VLMs process and interpret bistable images under different visual conditions.

Moreover, the paper introduced a dataset comprising 29 bistable images categorized into seven distinct types, sourced from online platforms and academic studies . This dataset included classic categories of bistable illusions like the Rubin Vase, Necker Cube, Duck-Rabbit, and Young-Old Woman, each with several iconic versions of the respective illusion type .

Overall, the paper's innovative approach lies in its comprehensive evaluation of VLMs on bistable images, introducing new methodologies for classification and generation tasks, and highlighting the sensitivity of VLMs to visual modifications and illusions. The study's detailed analysis and experimental setup provide valuable insights into the interaction between VLMs and cognitive illusions, offering a significant advancement in this research domain .


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 vision-language models and bistable images. Noteworthy researchers in this area include Jürgen Kornmeier, Michael Bach, Ranjay Krishna, Yuke Zhu, Oliver Groth, and many others . These researchers have contributed to various aspects of connecting language and vision, studying perceptual bias, and exploring the interaction between visual perception and language understanding.

The key to the solution mentioned in the paper "Evaluating Vision-Language Models on Bistable Images" involves utilizing a dataset comprising 29 bistable images categorized into seven distinct types, sourced from online platforms and academic studies. These images include classic categories of bistable illusions like the Rubin Vase, Necker Cube, Duck-Rabbit, and Young-Old Woman. The study explores the influence of visual modifications on perception by creating 116 variations for each image through controlled manipulations such as adjustments to image brightness, application of color tints, and image rotations . The experimental setup involved evaluating six Vision-Language Model families to assess the models' performance on these bistable images .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate Vision-Language Models (VLMs) on bistable images. The study conducted an extensive examination of VLMs using a dataset of 29 bistable images, along with their associated labels, subjected to 116 different manipulations in brightness, tint, and rotation . The dataset comprised seven distinct types of bistable images, sourced from online platforms and academic studies, including classic categories like the Rubin Vase, Necker Cube, Duck-Rabbit, and Young-Old Woman . To explore the influence of visual modifications on perception, the study created variations for each image through controlled manipulations such as adjustments in brightness, application of color tints (red, green, blue, yellow, magenta, cyan), and image rotations from 0 to 360 degrees every 10 degrees .


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

The dataset used for quantitative evaluation in the study is called "Obelics: An open web-scale filtered dataset of interleaved image-text documents" . The code for this dataset 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 "Evaluating Vision-Language Models on Bistable Images" offer substantial support for the scientific hypotheses that require verification. The study conducted a comprehensive examination of vision-language models using bistable images, which are visual stimuli that can be perceived in two distinct interpretations . The researchers manually curated a dataset of 29 bistable images and subjected them to various manipulations in brightness, tint, and rotation, evaluating twelve different models across six model architectures .

The findings from the study revealed that, with a few exceptions, there was a clear preference among the models for one interpretation over another, indicating minimal variance under image manipulations . This suggests that the models exhibited consistent behavior in their interpretations of the bistable images. Additionally, the comparison between the models' preferences and human interpretations highlighted differences, showing that the models do not always align with human biases and initial perceptions .

Moreover, the research delved into the influence of variations in prompts and the use of synonymous labels on model interpretations. The study discovered that these factors significantly impacted model interpretations more than image manipulations, emphasizing the substantial influence of language priors on bistable image interpretations compared to image-text training data . This analysis underscores the importance of considering linguistic cues in understanding how vision-language models interpret ambiguous visual stimuli.

In conclusion, the experiments and results presented in the paper provide robust support for the scientific hypotheses that needed verification by offering insights into the behavior of vision-language models when presented with bistable images and highlighting the impact of language priors on their interpretations .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Architectural Differences: The study summarizes the architectural variances of the models used, as detailed in Table 1 of the paper .
  • Dataset Information: It lists the datasets used for pre-training and instruction tuning in Table 2 of the paper .
  • Research Support: The research was supported by a gift from AWS AI for research in Trustworthy AI, emphasizing the importance of trustworthy AI in the study .

What work can be continued in depth?

Further research in this area can delve deeper into several aspects:

  • Exploring the impact of training data: Investigating how different training datasets influence the preferences and interpretations of vision-language models when presented with ambiguous images .
  • Analyzing the role of language model priors: Understanding how the base language models (LLMs) used during training affect the interpretation of bistable images by vision-language models, highlighting the significance of LLM priors in processing visual ambiguity .
  • Investigating the effect of textual modifications: Studying how variations in prompts and the use of synonymous labels impact model interpretations of ambiguous images, emphasizing the importance of language model priors in guiding vision-language models' responses .
  • Comparing with traditional vision models: Contrasting the handling of visual ambiguity by vision-language models with traditional convolutional neural networks (CNNs) that focus on geometric optical illusions, showcasing the differences in biases and interpretations influenced by language model priors .
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