Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models (2412.09645v2)

Abstract: Recent advancements in visual generative models have enabled high-quality image and video generation, opening diverse applications. However, evaluating these models often demands sampling hundreds or thousands of images or videos, making the process computationally expensive, especially for diffusion-based models with inherently slow sampling. Moreover, existing evaluation methods rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations. In contrast, humans can quickly form impressions of a model's capabilities by observing only a few samples. To mimic this, we propose the Evaluation Agent framework, which employs human-like strategies for efficient, dynamic, multi-round evaluations using only a few samples per round, while offering detailed, user-tailored analyses. It offers four key advantages: 1) efficiency, 2) promptable evaluation tailored to diverse user needs, 3) explainability beyond single numerical scores, and 4) scalability across various models and tools. Experiments show that Evaluation Agent reduces evaluation time to 10% of traditional methods while delivering comparable results. The Evaluation Agent framework is fully open-sourced to advance research in visual generative models and their efficient evaluation.

• The paper proposes an Evaluation Agent that reduces evaluation time to 10% of conventional methods while maintaining comparable accuracy.
• It employs a two-stage, LLM-based framework with a Proposal and Execution stage to dynamically guide prompt generation and iterative evaluations.
• Its flexible design offers explainable, scalable assessments for rapid model comparisons and personalized recommendations in visual generative tasks.

Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models

This paper introduces a novel framework, termed the "Evaluation Agent," designed to enhance the efficiency and adaptability in evaluating visual generative models. The motivation for this work stems from existing challenges in the evaluation of these models, particularly the computational costs associated with sampling numerous images or videos, which is especially pronounced in diffusion-based models. Furthermore, current evaluation approaches commonly employ rigid pipelines, limiting flexibility and providing results that lack comprehensive interpretation beyond numerical scores.

The Evaluation Agent framework addresses these limitations by adopting human-like strategies for evaluation, thus offering four key advantages: efficiency, promptable evaluation, explainability, and scalability. The framework is structured to iteratively evaluate models through multiple rounds, employing a dynamic mechanism that requires minimal samples per round and adjusts its evaluation based on intermediate outcomes. This approach mimics human evaluators who often make quick assessments with limited data.

Experiments underscored the framework's capability in reducing evaluation time to merely 10% of what traditional methods require, while maintaining comparable evaluation results. With a reduction in sample requirements to approximately 4-400, depending on setup, the framework achieves evaluation accuracy comparable to benchmark systems like VBench and T2I-CompBench. Furthermore, the Evaluation Agent's ability to deliver detailed, interpretable insights enhances accessibility for a broader audience, from experts to non-experts.

In terms of implementation, the framework leverages an LLM-based agent design, including a two-stage process encompassing a Proposal Stage—comprising the Plan Agent and PromptGen Agent—and an Execution Stage to dynamically engage with evaluation tasks. The Plan Agent is tasked with guiding the evaluation process based on user-defined criteria and intermediate results, while the PromptGen Agent designs prompts that align with the evaluation path specified by the Plan Agent. This interaction results in a versatile system that can cater to open-ended user queries, accommodating diverse user needs effectively.

The framework's potential applications are broad, facilitating rapid model comparisons and personalized model recommendations based on specific user criteria. This aligns well with an increasing demand for evaluations tailored to unique requirements in various applications of visual generative models such as content creation and design inspiration.

While the introduction of the Evaluation Agent represents a significant step in evaluation practices, its performance is intrinsically linked to the quality of the Evaluation Toolkit and the capabilities of the LLMs employed. Future research directions could explore further enhancements in these areas to improve evaluation robustness and scope handling.

In conclusion, the Evaluation Agent stands as a promising direction for the evaluation of visual generative models, offering significant reductions in evaluation complexity and time. Its open-source nature invites further research and development, potentially leading to even more efficient and flexible artificial intelligence systems in the visual domain. As the field progresses, the principles underlying this framework could inform the development of other AI evaluative systems, effectively reshaping evaluation methodologies in the field of machine learning and AI.