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Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction (2402.17969v1)

Published 28 Feb 2024 in cs.CV and cs.AI

Abstract: Given the accelerating progress of vision and LLMing, accurate evaluation of machine-generated image captions remains critical. In order to evaluate captions more closely to human preferences, metrics need to discriminate between captions of varying quality and content. However, conventional metrics fail short of comparing beyond superficial matches of words or embedding similarities; thus, they still need improvement. This paper presents VisCE$2$, a vision LLM-based caption evaluation method. Our method focuses on visual context, which refers to the detailed content of images, including objects, attributes, and relationships. By extracting and organizing them into a structured format, we replace the human-written references with visual contexts and help VLMs better understand the image, enhancing evaluation performance. Through meta-evaluation on multiple datasets, we validated that VisCE$2$ outperforms the conventional pre-trained metrics in capturing caption quality and demonstrates superior consistency with human judgment.

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Vision-LLM-based Caption Evaluation with Visual Context Extraction

Introduction

In the domain of vision and LLMing, the accurate assessment of machine-generated image captions is pivotal for gauging model effectiveness in describing visual observations through text. Traditional evaluation metrics, however, often fall short by focusing merely on superficial word matches or embedding similarities, thereby necessitating more refined methods. This paper introduces VisCE22², a novel evaluation method rooted in vision-LLMs (VLMs), emphasizing visual context extraction to bridge this gap. By structuring detailed visual contexts, including objects, attributes, and their relationships, VisCE22² aims to improve the alignment of caption evaluations with human judgment. The methodology's superior performance over conventional metrics is validated through extensive meta-evaluation across multiple datasets.

Methodology Overview

VisCE22² leverages VLMs for extracting and evaluating the visual context of images in tandem with candidate captions. This approach comprises two main components:

  • Visual Context Extraction: Detailed visual information is captured and presented in a structured format, emphasizing the objects, their attributes, and interrelations within the image.
  • VLM-based Caption Evaluation: Utilizing the extracted visual context, the candidate caption is evaluated against the image content, producing a score that reflects the accuracy and coverage of the caption.

This structured approach ensures a comprehensive understanding of the visual content, facilitating a more nuanced and accurate evaluation of captions.

Experimental Insights

Evaluation across various datasets indicates that VisCE22² outperforms existing metrics in terms of reflecting human judgment accuracy. Specifically, the method demonstrated an exceptional ability to discern the precision of captions, showcasing significantly higher consistency with human ratings compared to traditional metrics. The employment of visual context catalyzes better discrimination between captions of varying quality, addressing both the presence and the descriptive accuracy of objects and their interactions in the image.

Comparative Analysis

VisCE22²'s superiority is further substantiated through a comparative paper with both reference-based and reference-free metrics, including BLEU, ROUGE, CIDEr, SPICE, and CLIP-S. The method exhibits marked improvement over these metrics, underlining the limitations of reliance on n-gram matches or embedding similarities alone. Through detailed visualization of score distributions across datasets, the paper highlights how VisCE22² achieves a more granulated and realistic evaluation spectrum, closely mirroring human judgment.

Implications and Future Directions

The introduction of VisCE22² represents a significant step forward in the evaluation of image captions, showcasing the potential of integrating visual context in VLM-based methodologies. This advance not only contributes to the theoretical understanding of model evaluation but also has practical implications for future model development and benchmarking. Looking ahead, exploring the application of VisCE22² across a broader range of vision-LLMing tasks could further cement its utility and adaptability.

Limitations and Ethical Considerations

While the computational demand of VisCE22² is higher than traditional metrics due to its reliance on VLMs for context extraction and evaluation, ongoing advancements in model efficiency could mitigate this concern. Additionally, the method's performance is sensitive to the quality of prompts provided to the VLMs, underscoring the need for careful prompt design to ensure reliable evaluations. Ethically, since VisCE22² is focused on enhancing evaluation accuracy, negative impacts are minimized, though vigilance remains essential in broader machine learning applications.

Conclusion

The VisCE22² methodology heralds a new era in the evaluation of machine-generated image captions, embodying a more holistic and accurate reflection of human judgment by incorporating detailed visual context. Through rigorous experimentation and comparative analysis, the research underscores the method's effectiveness and sets the stage for its adoption and adaptation in future VLM endeavors.

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Authors (4)
  1. Koki Maeda (6 papers)
  2. Shuhei Kurita (21 papers)
  3. Taiki Miyanishi (9 papers)
  4. Naoaki Okazaki (70 papers)
Citations (1)
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