Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction
(2402.17969)Abstract
Given the accelerating progress of vision and language modeling, 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 language model-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.
Overview
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VisCE22² introduces a novel vision-language model (VLM)-based caption evaluation method focused on detailed visual context extraction, aiming to closely align with human judgment.
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The methodology employs a two-part process including visual context extraction to capture objects, attributes, and relationships, and VLM-based caption evaluation against the image content.
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Experimental results demonstrate VisCE22²'s superiority over traditional evaluation metrics by more accurately reflecting human judgment and differentiating caption quality.
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Future directions highlight the potential of VisCE22² in broadening the application of VLM-based tasks, despite computational demands and sensitivity to prompt quality.
Vision-Language Model-based Caption Evaluation with Visual Context Extraction
Introduction
In the domain of vision and language modeling, 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-language models (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 study 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 study 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-language modeling 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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