Why are Visually-Grounded Language Models Bad at Image Classification?

Yuhui Zhang, Alyssa Unell, Xiaohan Wang, Dhruba Ghosh, Yuchang Su, Ludwig Schmidt, Serena Yeung-Levy·May 28, 2024

Summary

This study highlights the underperformance of visually-grounded language models (VLMs) like GPT-4V and LLaVA in image classification tasks compared to dedicated models like CLIP, primarily due to a lack of sufficient training data. By integrating classification-focused datasets, VLMs can improve, as demonstrated by a 11.8% increase in accuracy on the ImageWikiQA dataset. The research also explores the impact of data type, prompting, and inference strategies, revealing that while VLMs can be enhanced with proper training, they still lag behind CLIP models in terms of accuracy. The study emphasizes the importance of data in determining VLM performance and suggests that future work should focus on addressing this issue and preventing catastrophic forgetting during fine-tuning.

Key findings

6

Paper digest

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

The paper aims to address the issue of visually-grounded language models (VLMs) performing poorly at image classification tasks compared to traditional classification models like CLIP . This problem is not new, as the research delves into investigating why VLMs struggle with image classification and explores potential solutions to enhance their performance . The primary focus is on understanding the reasons behind the underperformance of VLMs in classification settings, such as inference strategies, training methods, and data utilization . The study emphasizes the importance of data in improving VLMs' classification capabilities and suggests integrating traditional classification-focused datasets into VLM training as a method to enhance their overall performance .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that the primary reason visually-grounded language models (VLMs) underperform in image classification is due to data . The study investigates various aspects related to VLMs' inference, training, and data to understand why they struggle in classification tasks. It emphasizes that the critical information required for image classification is encoded in the VLM's latent space but can only be effectively decoded with sufficient and appropriate training data . The analysis suggests a strong correlation between the presence of classes during VLM training and the model's performance in those classes, highlighting the significance of data in enhancing VLM capabilities .


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

The paper "Why are Visually-Grounded Language Models Bad at Image Classification?" proposes several new ideas, methods, and models to address the limitations of visually-grounded language models (VLMs) in image classification :

  1. Feature Extraction and Linear Probing:

    • The paper introduces a method to extract features from the last layer of VLMs by using specific prompts like "USER: <576 Image Tokens> What type of object is in this photo? ASSISTANT:" or "<Image Tokens> Question: What type of object is in this photo? Answer:" .
    • It employs linear probing techniques to train a linear layer on the training set with specific parameters like a batch size of 512, a learning rate of 1e-3 using the Adam optimizer, and for 500 epochs to achieve the best performance on the validation set .
  2. ImageWikiQA Dataset:

    • The paper introduces the ImageWikiQA dataset, which is an object-centric, knowledge-intensive question-answering dataset that combines image classification and question answering. This dataset consists of multiple-choice questions based on Wikipedia pages of ImageNet classes, aiming to bridge the gap between classification and advanced capabilities .
  3. Evaluation of VLMs:

    • The paper evaluates various VLMs on the ImageWikiQA dataset and finds that current state-of-the-art VLMs perform poorly in answering questions given images. For instance, GPT4 achieves 100% accuracy when provided with the ground-truth class name but only achieves 61.2% accuracy with images .
    • It compares the performance of different VLMs, including public VLMs like BLIP2-2.7B, IBLIP-7B, LLaVA1.5-7B, and proprietary VLMs like GeminiPro and Claude3, highlighting the need for integrating classification data into VLM training to enhance their classification and overall capabilities .
  4. Training Objective and Data Details:

    • The paper discusses the training objectives for VLMs, such as fine-tuning the MLP projector between CLIP and the language model or fine-tuning both the MLP projector and the LM using LoRA. It emphasizes the importance of incorporating classification data into VLM training to improve their classification accuracy and general capabilities .
    • It provides insights into the data details, including the datasets used for training and validation, such as ImageNet, Flowers102, StanfordCars, Cars196, and Caltech101, to evaluate the performance of VLMs on image classification tasks .

Overall, the paper proposes innovative approaches, datasets, and evaluation metrics to enhance the performance of visually-grounded language models in image classification tasks, emphasizing the importance of integrating classification data into VLM training for improved capabilities. The paper "Why are Visually-Grounded Language Models Bad at Image Classification?" introduces novel characteristics and advantages compared to previous methods in the following aspects, supported by references to details in the paper:

  1. Feature Extraction and Linear Probing:

    • The paper proposes a method to extract features from the last layer of Visually-Grounded Language Models (VLMs) by utilizing specific prompts like "USER: <576 Image Tokens> What type of object is in this photo? ASSISTANT:" or "<Image Tokens> Question: What type of object is in this photo? Answer:" .
    • By employing linear probing techniques, the paper trains a linear layer on the training set with specific parameters like a batch size of 512, a learning rate of 1e-3 using the Adam optimizer, and for 500 epochs to achieve optimal performance on the validation set .
  2. ImageWikiQA Dataset:

    • The paper introduces the ImageWikiQA dataset, which is an object-centric, knowledge-intensive question-answering dataset that combines image classification and question answering. This dataset consists of multiple-choice questions based on Wikipedia pages of ImageNet classes, aiming to bridge the gap between classification and advanced capabilities .
    • By fine-tuning VLMs on the ImageNet classification dataset, the paper achieves substantially higher accuracy in recognizing objects and providing accurate answers to non-classification questions, outperforming pre-trained VLMs by 11.8%. This highlights the advantage of integrating classification data into VLM training to enhance performance .
  3. Evaluation of VLMs:

    • The paper evaluates various VLMs on the ImageWikiQA dataset and finds that current VLMs perform poorly in answering questions given images. For instance, GPT4 achieves 100% accuracy when provided with the ground-truth class name but only achieves 61.2% accuracy with images .
    • By comparing the performance of different VLMs, including public VLMs like BLIP2-2.7B, IBLIP-7B, LLaVA1.5-7B, and proprietary VLMs like GeminiPro and Claude3, the paper emphasizes the need to integrate classification data into VLM training to enhance their classification and overall capabilities .

Overall, the paper's innovative approaches, datasets, and evaluation metrics provide a comprehensive analysis of the limitations of VLMs in image classification and propose effective strategies to improve their performance by integrating classification data into training, thereby enhancing their classification accuracy and general capabilities.


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 have been conducted on the topic of visually-grounded language models (VLMs) and their performance in image classification . Noteworthy researchers in this field include the authors of the paper "Why are Visually-Grounded Language Models Bad at Image Classification?" which investigates the reasons behind the underperformance of VLMs in classification tasks . The key solution proposed in the paper to enhance VLMs is to integrate classification-focused datasets into their training process . By incorporating classification data during training, VLMs can improve their classification performance and overall capabilities, serving as a foundation for more advanced visual tasks like visual question answering .


How were the experiments in the paper designed?

The experiments in the paper "Why are Visually-Grounded Language Models Bad at Image Classification?" were designed to investigate the reasons behind the underperformance of visually-grounded language models (VLMs) in image classification settings . The experiments focused on exploring hypotheses related to VLMs' inference, training, and data processing . The analysis delved into factors such as prompt variations, label set size, inference strategy, information loss, training objectives, and the correlation between class exposure during VLM training and performance . The primary emphasis was on understanding the impact of data on VLM performance, highlighting the importance of proper training data for decoding critical information encoded in the VLM's latent space . Additionally, the experiments involved integrating classification-focused datasets into VLM training to enhance the models' general capabilities, leading to improved classification performance and more advanced visual capabilities .


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

The dataset used for quantitative evaluation in the study is comprised of four widely-used image classification benchmarks: ImageNet, Flowers102, StanfordCars, and Caltech101 . The code used in the evaluation is not explicitly mentioned to be 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 strong support for the scientific hypotheses that needed verification regarding visually-grounded language models (VLMs) and their performance in image classification . The study thoroughly investigates various aspects such as VLMs' inference, training, and data to understand why VLMs underperform in classification settings . The analysis reveals that the primary reason for the performance gap observed is the lack of proper training data, indicating a strong correlation between class presence during VLM training and performance in those classes . Additionally, the study demonstrates that training VLMs on classification datasets can achieve performance levels comparable to state-of-the-art classification models .

Moreover, the paper introduces ImageWikiQA, an object-centric question answering dataset that combines classification and advanced capabilities to bridge the gap between the two . By fine-tuning VLMs on the ImageNet classification dataset, the study shows a substantial improvement in recognizing objects and providing accurate answers to non-classification questions, outperforming pre-trained VLMs by 11.8% . This suggests that integrating traditional classification data into VLM training can significantly enhance VLM performance and pave the way for more advanced visual capabilities .

Overall, the experiments and results presented in the paper offer comprehensive and compelling evidence to support the scientific hypotheses related to the challenges faced by visually-grounded language models in image classification and the effectiveness of integrating classification data into VLM training to enhance their overall capabilities .


What are the contributions of this paper?

The paper "Why are Visually-Grounded Language Models Bad at Image Classification?" makes several key contributions:

  1. Identification of Underperformance: The paper identifies that visually-grounded language models (VLMs) significantly underperform in image classification compared to state-of-the-art classification models like CLIP .

  2. Investigation of Reasons for Underperformance: The study investigates various hypotheses regarding VLMs' inference, training, and data to understand why VLMs struggle in classification settings. It highlights that the primary reason for the performance gap is the lack of sufficient data during VLM training .

  3. Proposal for Improvement: Based on the analysis, the paper proposes a method to enhance VLMs' general capabilities by integrating traditional classification-focused datasets into VLM training. This integration leads to improved classification performance, laying the foundation for more advanced visual capabilities .

  4. Creation of ImageWikiQA Dataset: To validate the hypothesis and improvements, the paper introduces the ImageWikiQA dataset. This dataset contains complex real-world questions about ImageNet objects and demonstrates that VLMs fine-tuned on ImageNet classification data achieve higher accuracy in recognizing objects and answering non-classification questions .

  5. Performance Evaluation of VLMs: The paper evaluates the performance of various VLMs on the ImageWikiQA dataset, showing that current state-of-the-art VLMs struggle to answer questions based on images. For instance, GPT4 achieves 100% accuracy when provided with the ground-truth class name but only achieves 61.2% accuracy with images .

Overall, the paper contributes to the understanding of why VLMs face challenges in image classification, proposes a method to enhance their capabilities, and provides a dataset for evaluating VLM performance in recognizing objects and answering questions based on images .


What work can be continued in depth?

Further research in this area can delve deeper into the integration of traditional classification-focused datasets into visually-grounded language models (VLMs) training to enhance their performance . This approach has shown promising results in improving the general capabilities of VLMs, leading to advancements in tasks such as visual question answering . Additionally, exploring the impact of incorporating classification data on VLMs in real-world applications, such as virtual assistants for visually impaired individuals, could be a valuable direction for future studies .

Tables

10

Introduction
Background
Underperformance of VLMs like GPT-4V and LLaVA
CLIP's dominance in image classification
Objective
To investigate VLM performance enhancement through data integration
Identify factors affecting accuracy and data requirements
Method
Data Collection
Datasets and Integration
Image classification-focused datasets (e.g., ImageWikiQA)
Comparison with CLIP's training data
Data Augmentation and Expansion
Strategies for enhancing VLM datasets
Data Preprocessing
Techniques for preparing data for VLM fine-tuning
Handling imbalanced or diverse datasets
Model Evaluation
Performance Analysis
Accuracy improvements on ImageWikiQA (11.8% increase)
Comparison of VLMs and CLIP models in terms of accuracy
Factors Impacting Performance
Data Type
Effect of different types of image and text data on VLMs
Prompting Strategies
Exploration of effective prompts for VLMs in image classification
Inference Techniques
Investigating optimal inference strategies for VLMs
Limitations and Future Directions
Catastrophic Forgetting
Addressing the issue of forgetting learned knowledge during fine-tuning
Data Requirements and Importance
The role of data in determining VLM performance
Suggestions for future research and improvements
Conclusion
Summary of findings and implications for VLM development
Recommendations for enhancing VLMs in image classification tasks.
Basic info
papers
computer vision and pattern recognition
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How much accuracy improvement did VLMs experience when integrated with classification-focused datasets, as mentioned in the study?
According to the research, what aspect of VLMs limits their performance compared to dedicated models like CLIP?
What is the primary reason for the underperformance of VLMs like GPT-4V and LLaVA in image classification tasks?
What does the study suggest future work in the field of visually-grounded language models should focus on?

Why are Visually-Grounded Language Models Bad at Image Classification?

Yuhui Zhang, Alyssa Unell, Xiaohan Wang, Dhruba Ghosh, Yuchang Su, Ludwig Schmidt, Serena Yeung-Levy·May 28, 2024

Summary

This study highlights the underperformance of visually-grounded language models (VLMs) like GPT-4V and LLaVA in image classification tasks compared to dedicated models like CLIP, primarily due to a lack of sufficient training data. By integrating classification-focused datasets, VLMs can improve, as demonstrated by a 11.8% increase in accuracy on the ImageWikiQA dataset. The research also explores the impact of data type, prompting, and inference strategies, revealing that while VLMs can be enhanced with proper training, they still lag behind CLIP models in terms of accuracy. The study emphasizes the importance of data in determining VLM performance and suggests that future work should focus on addressing this issue and preventing catastrophic forgetting during fine-tuning.
Mind map
Investigating optimal inference strategies for VLMs
Exploration of effective prompts for VLMs in image classification
Effect of different types of image and text data on VLMs
Strategies for enhancing VLM datasets
Comparison with CLIP's training data
Image classification-focused datasets (e.g., ImageWikiQA)
Suggestions for future research and improvements
The role of data in determining VLM performance
Addressing the issue of forgetting learned knowledge during fine-tuning
Inference Techniques
Prompting Strategies
Data Type
Comparison of VLMs and CLIP models in terms of accuracy
Accuracy improvements on ImageWikiQA (11.8% increase)
Handling imbalanced or diverse datasets
Techniques for preparing data for VLM fine-tuning
Data Augmentation and Expansion
Datasets and Integration
Identify factors affecting accuracy and data requirements
To investigate VLM performance enhancement through data integration
CLIP's dominance in image classification
Underperformance of VLMs like GPT-4V and LLaVA
Recommendations for enhancing VLMs in image classification tasks.
Summary of findings and implications for VLM development
Data Requirements and Importance
Catastrophic Forgetting
Factors Impacting Performance
Performance Analysis
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Limitations and Future Directions
Model Evaluation
Method
Introduction
Outline
Introduction
Background
Underperformance of VLMs like GPT-4V and LLaVA
CLIP's dominance in image classification
Objective
To investigate VLM performance enhancement through data integration
Identify factors affecting accuracy and data requirements
Method
Data Collection
Datasets and Integration
Image classification-focused datasets (e.g., ImageWikiQA)
Comparison with CLIP's training data
Data Augmentation and Expansion
Strategies for enhancing VLM datasets
Data Preprocessing
Techniques for preparing data for VLM fine-tuning
Handling imbalanced or diverse datasets
Model Evaluation
Performance Analysis
Accuracy improvements on ImageWikiQA (11.8% increase)
Comparison of VLMs and CLIP models in terms of accuracy
Factors Impacting Performance
Data Type
Effect of different types of image and text data on VLMs
Prompting Strategies
Exploration of effective prompts for VLMs in image classification
Inference Techniques
Investigating optimal inference strategies for VLMs
Limitations and Future Directions
Catastrophic Forgetting
Addressing the issue of forgetting learned knowledge during fine-tuning
Data Requirements and Importance
The role of data in determining VLM performance
Suggestions for future research and improvements
Conclusion
Summary of findings and implications for VLM development
Recommendations for enhancing VLMs in image classification tasks.
Key findings
6

Paper digest

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

The paper aims to address the issue of visually-grounded language models (VLMs) performing poorly at image classification tasks compared to traditional classification models like CLIP . This problem is not new, as the research delves into investigating why VLMs struggle with image classification and explores potential solutions to enhance their performance . The primary focus is on understanding the reasons behind the underperformance of VLMs in classification settings, such as inference strategies, training methods, and data utilization . The study emphasizes the importance of data in improving VLMs' classification capabilities and suggests integrating traditional classification-focused datasets into VLM training as a method to enhance their overall performance .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that the primary reason visually-grounded language models (VLMs) underperform in image classification is due to data . The study investigates various aspects related to VLMs' inference, training, and data to understand why they struggle in classification tasks. It emphasizes that the critical information required for image classification is encoded in the VLM's latent space but can only be effectively decoded with sufficient and appropriate training data . The analysis suggests a strong correlation between the presence of classes during VLM training and the model's performance in those classes, highlighting the significance of data in enhancing VLM capabilities .


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

The paper "Why are Visually-Grounded Language Models Bad at Image Classification?" proposes several new ideas, methods, and models to address the limitations of visually-grounded language models (VLMs) in image classification :

  1. Feature Extraction and Linear Probing:

    • The paper introduces a method to extract features from the last layer of VLMs by using specific prompts like "USER: <576 Image Tokens> What type of object is in this photo? ASSISTANT:" or "<Image Tokens> Question: What type of object is in this photo? Answer:" .
    • It employs linear probing techniques to train a linear layer on the training set with specific parameters like a batch size of 512, a learning rate of 1e-3 using the Adam optimizer, and for 500 epochs to achieve the best performance on the validation set .
  2. ImageWikiQA Dataset:

    • The paper introduces the ImageWikiQA dataset, which is an object-centric, knowledge-intensive question-answering dataset that combines image classification and question answering. This dataset consists of multiple-choice questions based on Wikipedia pages of ImageNet classes, aiming to bridge the gap between classification and advanced capabilities .
  3. Evaluation of VLMs:

    • The paper evaluates various VLMs on the ImageWikiQA dataset and finds that current state-of-the-art VLMs perform poorly in answering questions given images. For instance, GPT4 achieves 100% accuracy when provided with the ground-truth class name but only achieves 61.2% accuracy with images .
    • It compares the performance of different VLMs, including public VLMs like BLIP2-2.7B, IBLIP-7B, LLaVA1.5-7B, and proprietary VLMs like GeminiPro and Claude3, highlighting the need for integrating classification data into VLM training to enhance their classification and overall capabilities .
  4. Training Objective and Data Details:

    • The paper discusses the training objectives for VLMs, such as fine-tuning the MLP projector between CLIP and the language model or fine-tuning both the MLP projector and the LM using LoRA. It emphasizes the importance of incorporating classification data into VLM training to improve their classification accuracy and general capabilities .
    • It provides insights into the data details, including the datasets used for training and validation, such as ImageNet, Flowers102, StanfordCars, Cars196, and Caltech101, to evaluate the performance of VLMs on image classification tasks .

Overall, the paper proposes innovative approaches, datasets, and evaluation metrics to enhance the performance of visually-grounded language models in image classification tasks, emphasizing the importance of integrating classification data into VLM training for improved capabilities. The paper "Why are Visually-Grounded Language Models Bad at Image Classification?" introduces novel characteristics and advantages compared to previous methods in the following aspects, supported by references to details in the paper:

  1. Feature Extraction and Linear Probing:

    • The paper proposes a method to extract features from the last layer of Visually-Grounded Language Models (VLMs) by utilizing specific prompts like "USER: <576 Image Tokens> What type of object is in this photo? ASSISTANT:" or "<Image Tokens> Question: What type of object is in this photo? Answer:" .
    • By employing linear probing techniques, the paper trains a linear layer on the training set with specific parameters like a batch size of 512, a learning rate of 1e-3 using the Adam optimizer, and for 500 epochs to achieve optimal performance on the validation set .
  2. ImageWikiQA Dataset:

    • The paper introduces the ImageWikiQA dataset, which is an object-centric, knowledge-intensive question-answering dataset that combines image classification and question answering. This dataset consists of multiple-choice questions based on Wikipedia pages of ImageNet classes, aiming to bridge the gap between classification and advanced capabilities .
    • By fine-tuning VLMs on the ImageNet classification dataset, the paper achieves substantially higher accuracy in recognizing objects and providing accurate answers to non-classification questions, outperforming pre-trained VLMs by 11.8%. This highlights the advantage of integrating classification data into VLM training to enhance performance .
  3. Evaluation of VLMs:

    • The paper evaluates various VLMs on the ImageWikiQA dataset and finds that current VLMs perform poorly in answering questions given images. For instance, GPT4 achieves 100% accuracy when provided with the ground-truth class name but only achieves 61.2% accuracy with images .
    • By comparing the performance of different VLMs, including public VLMs like BLIP2-2.7B, IBLIP-7B, LLaVA1.5-7B, and proprietary VLMs like GeminiPro and Claude3, the paper emphasizes the need to integrate classification data into VLM training to enhance their classification and overall capabilities .

Overall, the paper's innovative approaches, datasets, and evaluation metrics provide a comprehensive analysis of the limitations of VLMs in image classification and propose effective strategies to improve their performance by integrating classification data into training, thereby enhancing their classification accuracy and general capabilities.


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 have been conducted on the topic of visually-grounded language models (VLMs) and their performance in image classification . Noteworthy researchers in this field include the authors of the paper "Why are Visually-Grounded Language Models Bad at Image Classification?" which investigates the reasons behind the underperformance of VLMs in classification tasks . The key solution proposed in the paper to enhance VLMs is to integrate classification-focused datasets into their training process . By incorporating classification data during training, VLMs can improve their classification performance and overall capabilities, serving as a foundation for more advanced visual tasks like visual question answering .


How were the experiments in the paper designed?

The experiments in the paper "Why are Visually-Grounded Language Models Bad at Image Classification?" were designed to investigate the reasons behind the underperformance of visually-grounded language models (VLMs) in image classification settings . The experiments focused on exploring hypotheses related to VLMs' inference, training, and data processing . The analysis delved into factors such as prompt variations, label set size, inference strategy, information loss, training objectives, and the correlation between class exposure during VLM training and performance . The primary emphasis was on understanding the impact of data on VLM performance, highlighting the importance of proper training data for decoding critical information encoded in the VLM's latent space . Additionally, the experiments involved integrating classification-focused datasets into VLM training to enhance the models' general capabilities, leading to improved classification performance and more advanced visual capabilities .


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

The dataset used for quantitative evaluation in the study is comprised of four widely-used image classification benchmarks: ImageNet, Flowers102, StanfordCars, and Caltech101 . The code used in the evaluation is not explicitly mentioned to be 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 strong support for the scientific hypotheses that needed verification regarding visually-grounded language models (VLMs) and their performance in image classification . The study thoroughly investigates various aspects such as VLMs' inference, training, and data to understand why VLMs underperform in classification settings . The analysis reveals that the primary reason for the performance gap observed is the lack of proper training data, indicating a strong correlation between class presence during VLM training and performance in those classes . Additionally, the study demonstrates that training VLMs on classification datasets can achieve performance levels comparable to state-of-the-art classification models .

Moreover, the paper introduces ImageWikiQA, an object-centric question answering dataset that combines classification and advanced capabilities to bridge the gap between the two . By fine-tuning VLMs on the ImageNet classification dataset, the study shows a substantial improvement in recognizing objects and providing accurate answers to non-classification questions, outperforming pre-trained VLMs by 11.8% . This suggests that integrating traditional classification data into VLM training can significantly enhance VLM performance and pave the way for more advanced visual capabilities .

Overall, the experiments and results presented in the paper offer comprehensive and compelling evidence to support the scientific hypotheses related to the challenges faced by visually-grounded language models in image classification and the effectiveness of integrating classification data into VLM training to enhance their overall capabilities .


What are the contributions of this paper?

The paper "Why are Visually-Grounded Language Models Bad at Image Classification?" makes several key contributions:

  1. Identification of Underperformance: The paper identifies that visually-grounded language models (VLMs) significantly underperform in image classification compared to state-of-the-art classification models like CLIP .

  2. Investigation of Reasons for Underperformance: The study investigates various hypotheses regarding VLMs' inference, training, and data to understand why VLMs struggle in classification settings. It highlights that the primary reason for the performance gap is the lack of sufficient data during VLM training .

  3. Proposal for Improvement: Based on the analysis, the paper proposes a method to enhance VLMs' general capabilities by integrating traditional classification-focused datasets into VLM training. This integration leads to improved classification performance, laying the foundation for more advanced visual capabilities .

  4. Creation of ImageWikiQA Dataset: To validate the hypothesis and improvements, the paper introduces the ImageWikiQA dataset. This dataset contains complex real-world questions about ImageNet objects and demonstrates that VLMs fine-tuned on ImageNet classification data achieve higher accuracy in recognizing objects and answering non-classification questions .

  5. Performance Evaluation of VLMs: The paper evaluates the performance of various VLMs on the ImageWikiQA dataset, showing that current state-of-the-art VLMs struggle to answer questions based on images. For instance, GPT4 achieves 100% accuracy when provided with the ground-truth class name but only achieves 61.2% accuracy with images .

Overall, the paper contributes to the understanding of why VLMs face challenges in image classification, proposes a method to enhance their capabilities, and provides a dataset for evaluating VLM performance in recognizing objects and answering questions based on images .


What work can be continued in depth?

Further research in this area can delve deeper into the integration of traditional classification-focused datasets into visually-grounded language models (VLMs) training to enhance their performance . This approach has shown promising results in improving the general capabilities of VLMs, leading to advancements in tasks such as visual question answering . Additionally, exploring the impact of incorporating classification data on VLMs in real-world applications, such as virtual assistants for visually impaired individuals, could be a valuable direction for future studies .

Tables
10
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.