Quantifying Image Caption Concreteness for Multimodal Dataset Curation

Core Concepts

The authors propose the Image Caption Concreteness (ICC) metric to evaluate the visual concreteness of image captions without an image reference, enhancing dataset curation for efficient training in resource-constrained settings.

Abstract

The study introduces the ICC metric to quantify visual concreteness in image captions, demonstrating its effectiveness in selecting high-quality samples from multimodal datasets. By leveraging foundation models, ICC correlates strongly with human judgments and improves downstream tasks like captioning and representation learning.
Standard data filtering methods often fail to identify highly abstract or subjective captions that are semantically aligned with images but lack visual concreteness. The ICC metric addresses this gap by measuring text quality without an image reference.
The study showcases examples where ICC successfully differentiates between concrete and abstract captions, highlighting its importance in selecting high-quality samples for vision-and-language tasks.
By distilling scores from complex autoencoding pipelines into a computationally-efficient model, ICC enables fast inference on large datasets while maintaining accuracy in quantifying visual concreteness.
Overall, the research emphasizes the significance of measuring visual concreteness in image captions for effective dataset curation and improved performance in multimodal learning tasks.

Stats

"We demonstrate that this strongly correlates with human evaluation of concreteness in both single-word and sentence-level texts."
"Our results indicate a strong correlation between ICC and both single-word concreteness and caption text scores."

Quotes

"We demonstrate that this strongly correlates with human evaluation of concreteness in both single-word and sentence-level texts."
"Our results indicate a strong correlation between ICC and both single-word concreteness and caption text scores."

Key Insights Distilled From

ICC

by Moran Yanuka... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01306.pdf

Deeper Inquiries

How can the ICC metric be adapted to address biases inherited from training data?

The ICC metric can be adapted to address biases inherited from training data by incorporating bias detection and mitigation techniques into the model development process. One approach could involve conducting a thorough analysis of the dataset used for training the ICC metric to identify potential sources of bias, such as underrepresented or overrepresented groups, stereotypes, or cultural assumptions.
To address these biases, researchers can implement strategies like data augmentation with diverse samples, adversarial training to reduce bias in representations, or fairness constraints during model optimization. Additionally, post-hoc bias evaluation tools can be integrated into the pipeline to continuously monitor and mitigate biases that may arise during deployment.
By actively monitoring and addressing biases in both the dataset and model architecture, the ICC metric can be fine-tuned to produce more equitable and unbiased results across different demographic groups.

What potential ethical considerations should be taken into account before deploying models trained using the ICC metric?

Before deploying models trained using the ICC metric, several ethical considerations need to be carefully evaluated:

Bias Mitigation: Ensuring that measures are in place to detect and mitigate any inherent biases present in both the dataset used for training and within the model itself.

Transparency: Providing transparency about how decisions are made based on predictions generated by models utilizing the ICC metric.

Privacy: Safeguarding user privacy by implementing robust data protection protocols when handling sensitive information.

Accountability: Establishing clear accountability mechanisms for any unintended consequences resulting from model deployment.

Fairness: Ensuring fairness in decision-making processes derived from models trained with ICC metrics across various demographic groups without perpetuating existing disparities.

Informed Consent: Obtaining informed consent from individuals whose data is being used for training datasets if applicable.

Continuous Monitoring: Implementing ongoing monitoring systems post-deployment to assess performance against ethical standards and make necessary adjustments as needed.

How might increasing the scale of filtered datasets impact downstream model performance when using

the ICC metric?
Increasing
the scale of filtered datasets when using
the
ICC
metric has several implications on downstream
model performance:

Improved Generalization:
A larger dataset size often leads
to improved generalization capabilities,
allowing
models trained on such datasets
to perform better on unseen examples
and real-world scenarios.

Enhanced Robustness:
With a larger pool of diverse samples,
models become more robust against noise,
variance,
and outliers present in smaller datasets

**
Increased Complexity: **
As dataset size grows,
so does its complexity,
which may require more sophisticated modeling techniques
and computational resources

**
Longer Training Times: **
Training models on larger datasets typically requires longer timeframes due
to increased sample sizes,
leading
to higher computational costs

**
Potential Overfitting Risks: **
While large-scale datasets offer benefits,
there's also an increased risk of overfitting if not managed properly through regularization techniques
or validation strategies

**
Higher Performance Expectations: **
Larger filtered datasets set higher expectations for downstream model performance,
as stakeholders anticipate significant improvements due
to richer input information provided by a larger sample size