IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning

Soumya Suvra Ghosal, Samyadeep Basu, Soheil Feizi, Dinesh Manocha·June 19, 2024

Summary

IntCoOp is an interpretable prompt-tuning method for vision-language models like CLIP that enhances image-text alignment by incorporating compositional attributes into prompts. It addresses the need for manual prompt engineering by learning to align attribute-level biases and class embeddings during training. This results in improved performance in few-shot learning tasks, particularly in generalizing to novel classes and unseen domain shifts. IntCoOp outperforms state-of-the-art methods like CoOp by an average of 7.35% across 10 diverse datasets in a 16-shot setup. The study shows that attribute information enhances model robustness and generalization, with IntCoOp demonstrating its effectiveness through improved attribute extraction, better alignment, and faster computational efficiency compared to alternatives. The research contributes to the understanding of how interpretable prompts can boost model performance in vision-language tasks.

Key findings

21

Paper digest

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

The paper "IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning" aims to address the issue of prompt engineering by proposing a method to learn prompts directly from data, specifically focusing on learning interpretable attributes for images . This problem is not entirely new, as previous approaches like CoOp have also attempted to tackle prompt learning directly from data . The novelty lies in the proposed method, IntCoOp, which introduces a prompt-tuning approach that emphasizes interpretability and attribute extraction from images .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that incorporating compositional attributes, such as descriptive information about objects in images, into the design of manual prompts significantly enhances image-text alignment scores . The research focuses on integrating attribute-level inductive biases into the prompt-tuning process to generate more interpretable prompts . The study explores how prompts containing attribute information describing objects in images lead to improved image-text alignment scores in contrastive models like CLIP . The proposed method, IntCoOp, aligns attribute-level inductive biases and class embeddings during training to facilitate the generation of interpretable prompts .


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

The paper "IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning" introduces several novel ideas, methods, and models in the field of vision-language prompt tuning :

  1. IntCoOp Model: The paper proposes the IntCoOp model, which is designed to enhance performance on domain generalization tasks. IntCoOp is trained on the ImageNet dataset in a few-shot setup with 16 samples per class and evaluated on four domain-shifted ImageNet datasets. The model demonstrates improved performance compared to existing techniques .

  2. Visual Prompting: IntCoOp effectively utilizes deep visual prompting to enhance image representations. The model incorporates visual tokens into deeper transformer layers, which significantly boosts performance compared to a shallow prompting strategy. Visual prompting is crucial for training IntCoOp and plays a key role in its success .

  3. Instance Conditioning: The paper explores the use of a multi-head attention module for generating image-conditioned prompts during training. This approach conditions the prompts on the input image, contributing to the model's interpretability and performance. Instance conditioning is shown to be effective in generating accurate and relevant prompts .

  4. Attribute Learning: IntCoOp emphasizes the importance of learning contextually meaningful attributes during training. By generating prompts with interpretable compositional information, the model achieves improved performance. Learning accurate and relevant attributes is critical for the model's success, as demonstrated through experimental validation .

  5. Efficiency: The paper evaluates the computational efficiency of IntCoOp compared to other frameworks. It reports the training and inference times for IntCoOp, showcasing its computational performance. Despite a slightly higher training time due to instance-conditional prompt generation, IntCoOp demonstrates significant performance improvements .

  6. Generalization Capability: IntCoOp excels in base-to-novel class generalization tasks, outperforming state-of-the-art techniques. The model is trained on base classes within a few-shot framework and evaluated across both base and novel categories on diverse image classification datasets. This highlights the model's strong generalization ability .

Overall, the paper introduces the IntCoOp model with innovative approaches such as visual prompting, instance conditioning, and attribute learning, leading to enhanced performance on domain generalization tasks and demonstrating superior generalization capabilities compared to existing methods. The "IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning" paper introduces several key characteristics and advantages compared to previous methods, as detailed in the paper:

  1. Visual Prompting: IntCoOp utilizes deep visual prompting effectively to enhance image representations. The model incorporates visual tokens into deeper transformer layers, leading to a substantial performance boost compared to a shallow prompting strategy. Visual prompting plays a crucial role in training IntCoOp and significantly improves its overall performance .

  2. Instance Conditioning: Unlike prior studies that directly add the image embedding to the context vector, IntCoOp employs a multi-head attention module for generating image-conditioned prompts during training. This approach conditions the prompts on the input image, contributing to the model's interpretability and performance. Instance conditioning with multi-head attention is shown to be effective in generating accurate and relevant prompts .

  3. Attribute Learning: IntCoOp emphasizes the importance of learning contextually meaningful attributes during training. By generating prompts with interpretable compositional information, the model achieves improved performance. Experimental validation demonstrates that learning accurate and relevant attributes is crucial for the model's success .

  4. Efficiency: In terms of computational efficiency, IntCoOp is compared to other frameworks in terms of training and inference times. Despite a slightly higher training time due to instance-conditional prompt generation, IntCoOp demonstrates significant performance improvements. The model showcases improved robustness to distribution shifts, domain generalization, and few-shot learning, outperforming existing methods .

  5. Generalization Capability: IntCoOp excels in base-to-novel class generalization tasks, showcasing superior generalization capabilities compared to state-of-the-art techniques. The model is trained on base classes within a few-shot framework and evaluated across both base and novel categories on diverse image classification datasets. IntCoOp outperforms existing frameworks in domain generalization tasks, demonstrating improved robustness and performance .

Overall, the characteristics and advantages of IntCoOp, such as effective visual prompting, instance conditioning, attribute learning, computational efficiency, and strong generalization capabilities, position it as a promising approach in the field of vision-language prompt tuning, offering significant improvements over previous methods.


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 in the field of interpretability-aware vision-language prompt tuning. Noteworthy researchers in this area include Zhou et al., Khattak et al., Yao et al., Zhu et al., Bulat and Tzimiropoulos, Lee et al., Cho et al., Chen et al., and Ouali et al. .

The key solution proposed in the paper involves an interpretable prompt-tuning approach known as IntCoOp. This approach incorporates attribute information into the prompt-tuning process to generate more interpretable prompts. By integrating attribute information into the prompt-tuning procedure, the method aims to enhance image-text alignment scores in contrastive models like CLIP. The paper emphasizes the importance of learning interpretable concepts in prompts and demonstrates the effectiveness of generating attribute descriptions for images to improve model performance .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on evaluating the effectiveness of the proposed method, IntCoOp, in various scenarios . The experiments aimed to assess the impact of incorporating attribute-level inductive biases and class embeddings during prompt-tuning on tasks such as generalization to novel classes and robustness to distribution shifts . Additionally, the experiments involved comparing the performance of IntCoOp with other frameworks across diverse datasets to demonstrate its efficacy . The study also included ablation studies to analyze the importance of different design choices, such as loss functions and regularization parameters, in training IntCoOp .


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

The dataset used for quantitative evaluation in the study is a diverse set of 10 datasets, including ImageNet, Caltech101, OxfordPets, StanfordCars, Flowers102, Food101, FGVCAircraft, SUN397, EuroSAT, and UCF101 . The code for the IntCoOp method, which is the novel prompt-tuning approach presented in the research, 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. The study introduces a novel prompt-tuning method called IntCoOp, which aligns attribute-level inductive biases and class embeddings during training to generate interpretable prompts . The findings demonstrate that incorporating compositional attributes in prompts significantly enhances image-text alignment scores, leading to improved performance . Additionally, the study evaluates IntCoOp across various downstream datasets, showing generalization to novel classes and robustness to distribution shifts . The results indicate that IntCoOp outperforms existing frameworks, such as CoOp, by a significant margin, showcasing the effectiveness of integrating attribute-level inductive biases into the prompt-tuning process . Overall, the empirical evidence presented in the paper supports the hypothesis that incorporating attribute-level information into prompt-tuning can enhance the performance and interpretability of vision-language models.


What are the contributions of this paper?

The paper "IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning" makes the following key contributions:

  • Introduces a novel prompt-tuning method called IntCoOp that aligns attribute-level inductive biases and class embeddings during training to generate interpretable prompts .
  • Devises an efficient cross-attention mechanism to seamlessly integrate image information with learnable prompt tokens .
  • Conducts comprehensive experiments across various tasks, including generalization to unseen classes and distribution shifts, demonstrating the effectiveness of IntCoOp .

What work can be continued in depth?

Further research in this area can delve deeper into the following aspects:

  • Exploring the Impact of Attribute-Level Inductive Biases: Future studies can investigate the specific impact of incorporating attribute-level inductive biases into prompt-tuning methods like IntCoOp. This exploration can focus on understanding how these biases influence the interpretability and performance of vision-language models .
  • Enhancing Prompt Embeddings: Research can be conducted to optimize the prompt embeddings generated by IntCoOp. By refining the prompt embeddings to better align with attribute information, the effectiveness and interpretability of the prompts can be further improved .
  • Ablations on Design Choices: Conducting comprehensive ablations on the design choices made in prompt-tuning frameworks like IntCoOp can provide valuable insights. By analyzing different configurations and parameters, researchers can identify the most effective strategies for prompt-tuning in vision-language models .

Tables

4

Introduction
Background
Challenges of Manual Prompt Engineering
Difficulty in creating effective prompts for diverse tasks
The Rise of Vision-Language Models (CLIP)
Importance of aligning image and text representations
Objective
IntCoOp's Goal
To enhance image-text alignment with compositional attributes
Contributions
Improving few-shot learning performance and generalization
Method
Data Collection
Dataset Selection
Diverse set of 10 datasets for evaluation
Data Preparation
Incorporating attribute annotations for improved prompts
Data Preprocessing
Attribute-Level Bias Alignment
Learning to align attribute embeddings during training
Class Embeddings Integration
Combining class information with attribute-based prompts
Training Process
Model Architecture
Introducing IntCoOp to CLIP or similar models
Training Strategy
16-shot learning setup for benchmarking
Performance Metrics
Evaluation
Attribute extraction accuracy
Alignment Comparison
IntCoOp vs CoOp and other state-of-the-art methods
Computational Efficiency
Speed and Scalability
Faster computation compared to alternative approaches
Experiments and Results
Benchmarks
16-Shot Learning Results
Average improvement of 7.35% over CoOp and others
Novel Class Generalization
Enhanced performance on unseen classes
Case Studies
Domain Shift Analysis
Robustness across different domains
Attribute Extraction Analysis
Interpretable Prompt Impact
Improved attribute understanding by the model
Conclusion
Key Findings
The value of interpretable prompts in enhancing vision-language tasks
Future Directions
Potential applications and areas for further research
References
List of cited works and methodology details
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What is IntCoOp and how does it differ from traditional prompt-tuning methods for vision-language models?
In what ways does IntCoOp demonstrate its effectiveness compared to other methods, such as attribute extraction and computational efficiency?
How does IntCoOp address the issue of manual prompt engineering in image-text alignment?
What is the average improvement of IntCoOp over CoOp across 10 diverse datasets in a 16-shot setup?

IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning

Soumya Suvra Ghosal, Samyadeep Basu, Soheil Feizi, Dinesh Manocha·June 19, 2024

Summary

IntCoOp is an interpretable prompt-tuning method for vision-language models like CLIP that enhances image-text alignment by incorporating compositional attributes into prompts. It addresses the need for manual prompt engineering by learning to align attribute-level biases and class embeddings during training. This results in improved performance in few-shot learning tasks, particularly in generalizing to novel classes and unseen domain shifts. IntCoOp outperforms state-of-the-art methods like CoOp by an average of 7.35% across 10 diverse datasets in a 16-shot setup. The study shows that attribute information enhances model robustness and generalization, with IntCoOp demonstrating its effectiveness through improved attribute extraction, better alignment, and faster computational efficiency compared to alternatives. The research contributes to the understanding of how interpretable prompts can boost model performance in vision-language tasks.
Mind map
Improved attribute understanding by the model
Interpretable Prompt Impact
Robustness across different domains
Domain Shift Analysis
Enhanced performance on unseen classes
Novel Class Generalization
Average improvement of 7.35% over CoOp and others
16-Shot Learning Results
Faster computation compared to alternative approaches
Speed and Scalability
IntCoOp vs CoOp and other state-of-the-art methods
Alignment Comparison
Attribute extraction accuracy
Evaluation
16-shot learning setup for benchmarking
Training Strategy
Introducing IntCoOp to CLIP or similar models
Model Architecture
Combining class information with attribute-based prompts
Class Embeddings Integration
Learning to align attribute embeddings during training
Attribute-Level Bias Alignment
Incorporating attribute annotations for improved prompts
Data Preparation
Diverse set of 10 datasets for evaluation
Dataset Selection
Improving few-shot learning performance and generalization
Contributions
To enhance image-text alignment with compositional attributes
IntCoOp's Goal
Importance of aligning image and text representations
The Rise of Vision-Language Models (CLIP)
Difficulty in creating effective prompts for diverse tasks
Challenges of Manual Prompt Engineering
List of cited works and methodology details
Potential applications and areas for further research
Future Directions
The value of interpretable prompts in enhancing vision-language tasks
Key Findings
Attribute Extraction Analysis
Case Studies
Benchmarks
Computational Efficiency
Performance Metrics
Training Process
Data Preprocessing
Data Collection
Objective
Background
References
Conclusion
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Challenges of Manual Prompt Engineering
Difficulty in creating effective prompts for diverse tasks
The Rise of Vision-Language Models (CLIP)
Importance of aligning image and text representations
Objective
IntCoOp's Goal
To enhance image-text alignment with compositional attributes
Contributions
Improving few-shot learning performance and generalization
Method
Data Collection
Dataset Selection
Diverse set of 10 datasets for evaluation
Data Preparation
Incorporating attribute annotations for improved prompts
Data Preprocessing
Attribute-Level Bias Alignment
Learning to align attribute embeddings during training
Class Embeddings Integration
Combining class information with attribute-based prompts
Training Process
Model Architecture
Introducing IntCoOp to CLIP or similar models
Training Strategy
16-shot learning setup for benchmarking
Performance Metrics
Evaluation
Attribute extraction accuracy
Alignment Comparison
IntCoOp vs CoOp and other state-of-the-art methods
Computational Efficiency
Speed and Scalability
Faster computation compared to alternative approaches
Experiments and Results
Benchmarks
16-Shot Learning Results
Average improvement of 7.35% over CoOp and others
Novel Class Generalization
Enhanced performance on unseen classes
Case Studies
Domain Shift Analysis
Robustness across different domains
Attribute Extraction Analysis
Interpretable Prompt Impact
Improved attribute understanding by the model
Conclusion
Key Findings
The value of interpretable prompts in enhancing vision-language tasks
Future Directions
Potential applications and areas for further research
References
List of cited works and methodology details
Key findings
21

Paper digest

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

The paper "IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning" aims to address the issue of prompt engineering by proposing a method to learn prompts directly from data, specifically focusing on learning interpretable attributes for images . This problem is not entirely new, as previous approaches like CoOp have also attempted to tackle prompt learning directly from data . The novelty lies in the proposed method, IntCoOp, which introduces a prompt-tuning approach that emphasizes interpretability and attribute extraction from images .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that incorporating compositional attributes, such as descriptive information about objects in images, into the design of manual prompts significantly enhances image-text alignment scores . The research focuses on integrating attribute-level inductive biases into the prompt-tuning process to generate more interpretable prompts . The study explores how prompts containing attribute information describing objects in images lead to improved image-text alignment scores in contrastive models like CLIP . The proposed method, IntCoOp, aligns attribute-level inductive biases and class embeddings during training to facilitate the generation of interpretable prompts .


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

The paper "IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning" introduces several novel ideas, methods, and models in the field of vision-language prompt tuning :

  1. IntCoOp Model: The paper proposes the IntCoOp model, which is designed to enhance performance on domain generalization tasks. IntCoOp is trained on the ImageNet dataset in a few-shot setup with 16 samples per class and evaluated on four domain-shifted ImageNet datasets. The model demonstrates improved performance compared to existing techniques .

  2. Visual Prompting: IntCoOp effectively utilizes deep visual prompting to enhance image representations. The model incorporates visual tokens into deeper transformer layers, which significantly boosts performance compared to a shallow prompting strategy. Visual prompting is crucial for training IntCoOp and plays a key role in its success .

  3. Instance Conditioning: The paper explores the use of a multi-head attention module for generating image-conditioned prompts during training. This approach conditions the prompts on the input image, contributing to the model's interpretability and performance. Instance conditioning is shown to be effective in generating accurate and relevant prompts .

  4. Attribute Learning: IntCoOp emphasizes the importance of learning contextually meaningful attributes during training. By generating prompts with interpretable compositional information, the model achieves improved performance. Learning accurate and relevant attributes is critical for the model's success, as demonstrated through experimental validation .

  5. Efficiency: The paper evaluates the computational efficiency of IntCoOp compared to other frameworks. It reports the training and inference times for IntCoOp, showcasing its computational performance. Despite a slightly higher training time due to instance-conditional prompt generation, IntCoOp demonstrates significant performance improvements .

  6. Generalization Capability: IntCoOp excels in base-to-novel class generalization tasks, outperforming state-of-the-art techniques. The model is trained on base classes within a few-shot framework and evaluated across both base and novel categories on diverse image classification datasets. This highlights the model's strong generalization ability .

Overall, the paper introduces the IntCoOp model with innovative approaches such as visual prompting, instance conditioning, and attribute learning, leading to enhanced performance on domain generalization tasks and demonstrating superior generalization capabilities compared to existing methods. The "IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning" paper introduces several key characteristics and advantages compared to previous methods, as detailed in the paper:

  1. Visual Prompting: IntCoOp utilizes deep visual prompting effectively to enhance image representations. The model incorporates visual tokens into deeper transformer layers, leading to a substantial performance boost compared to a shallow prompting strategy. Visual prompting plays a crucial role in training IntCoOp and significantly improves its overall performance .

  2. Instance Conditioning: Unlike prior studies that directly add the image embedding to the context vector, IntCoOp employs a multi-head attention module for generating image-conditioned prompts during training. This approach conditions the prompts on the input image, contributing to the model's interpretability and performance. Instance conditioning with multi-head attention is shown to be effective in generating accurate and relevant prompts .

  3. Attribute Learning: IntCoOp emphasizes the importance of learning contextually meaningful attributes during training. By generating prompts with interpretable compositional information, the model achieves improved performance. Experimental validation demonstrates that learning accurate and relevant attributes is crucial for the model's success .

  4. Efficiency: In terms of computational efficiency, IntCoOp is compared to other frameworks in terms of training and inference times. Despite a slightly higher training time due to instance-conditional prompt generation, IntCoOp demonstrates significant performance improvements. The model showcases improved robustness to distribution shifts, domain generalization, and few-shot learning, outperforming existing methods .

  5. Generalization Capability: IntCoOp excels in base-to-novel class generalization tasks, showcasing superior generalization capabilities compared to state-of-the-art techniques. The model is trained on base classes within a few-shot framework and evaluated across both base and novel categories on diverse image classification datasets. IntCoOp outperforms existing frameworks in domain generalization tasks, demonstrating improved robustness and performance .

Overall, the characteristics and advantages of IntCoOp, such as effective visual prompting, instance conditioning, attribute learning, computational efficiency, and strong generalization capabilities, position it as a promising approach in the field of vision-language prompt tuning, offering significant improvements over previous methods.


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 in the field of interpretability-aware vision-language prompt tuning. Noteworthy researchers in this area include Zhou et al., Khattak et al., Yao et al., Zhu et al., Bulat and Tzimiropoulos, Lee et al., Cho et al., Chen et al., and Ouali et al. .

The key solution proposed in the paper involves an interpretable prompt-tuning approach known as IntCoOp. This approach incorporates attribute information into the prompt-tuning process to generate more interpretable prompts. By integrating attribute information into the prompt-tuning procedure, the method aims to enhance image-text alignment scores in contrastive models like CLIP. The paper emphasizes the importance of learning interpretable concepts in prompts and demonstrates the effectiveness of generating attribute descriptions for images to improve model performance .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on evaluating the effectiveness of the proposed method, IntCoOp, in various scenarios . The experiments aimed to assess the impact of incorporating attribute-level inductive biases and class embeddings during prompt-tuning on tasks such as generalization to novel classes and robustness to distribution shifts . Additionally, the experiments involved comparing the performance of IntCoOp with other frameworks across diverse datasets to demonstrate its efficacy . The study also included ablation studies to analyze the importance of different design choices, such as loss functions and regularization parameters, in training IntCoOp .


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

The dataset used for quantitative evaluation in the study is a diverse set of 10 datasets, including ImageNet, Caltech101, OxfordPets, StanfordCars, Flowers102, Food101, FGVCAircraft, SUN397, EuroSAT, and UCF101 . The code for the IntCoOp method, which is the novel prompt-tuning approach presented in the research, 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. The study introduces a novel prompt-tuning method called IntCoOp, which aligns attribute-level inductive biases and class embeddings during training to generate interpretable prompts . The findings demonstrate that incorporating compositional attributes in prompts significantly enhances image-text alignment scores, leading to improved performance . Additionally, the study evaluates IntCoOp across various downstream datasets, showing generalization to novel classes and robustness to distribution shifts . The results indicate that IntCoOp outperforms existing frameworks, such as CoOp, by a significant margin, showcasing the effectiveness of integrating attribute-level inductive biases into the prompt-tuning process . Overall, the empirical evidence presented in the paper supports the hypothesis that incorporating attribute-level information into prompt-tuning can enhance the performance and interpretability of vision-language models.


What are the contributions of this paper?

The paper "IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning" makes the following key contributions:

  • Introduces a novel prompt-tuning method called IntCoOp that aligns attribute-level inductive biases and class embeddings during training to generate interpretable prompts .
  • Devises an efficient cross-attention mechanism to seamlessly integrate image information with learnable prompt tokens .
  • Conducts comprehensive experiments across various tasks, including generalization to unseen classes and distribution shifts, demonstrating the effectiveness of IntCoOp .

What work can be continued in depth?

Further research in this area can delve deeper into the following aspects:

  • Exploring the Impact of Attribute-Level Inductive Biases: Future studies can investigate the specific impact of incorporating attribute-level inductive biases into prompt-tuning methods like IntCoOp. This exploration can focus on understanding how these biases influence the interpretability and performance of vision-language models .
  • Enhancing Prompt Embeddings: Research can be conducted to optimize the prompt embeddings generated by IntCoOp. By refining the prompt embeddings to better align with attribute information, the effectiveness and interpretability of the prompts can be further improved .
  • Ablations on Design Choices: Conducting comprehensive ablations on the design choices made in prompt-tuning frameworks like IntCoOp can provide valuable insights. By analyzing different configurations and parameters, researchers can identify the most effective strategies for prompt-tuning in vision-language models .
Tables
4
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