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A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark


Core Concepts
The authors propose a Unified Multi-modal Image Aesthetic Assessment (UNIAA) framework, including a Multi-modal Large Language Model (MLLM) named UNIAA-LLaVA and a comprehensive benchmark named UNIAA-Bench, to align with the human aesthetic process and achieve good results in multiple aesthetic subtasks.
Abstract
The authors propose the UNIAA framework to address the limitations of traditional IAA methods, which are typically constrained to a single dataset or task, restricting the universality and broader application. UNIAA includes: UNIAA-LLaVA: An MLLM baseline capable of unifying aesthetic perception, description, and assessment tasks. UNIAA-Bench: A comprehensive aesthetic benchmark that evaluates the aesthetic capabilities of MLLMs from three aspects - Aesthetic Perception, Aesthetic Description, and Aesthetic Assessment. To obtain the UNIAA-LLaVA, the authors establish a low-cost IAA Dataset Conversion Paradigm (IDCP) to transform existing aesthetic datasets into a format suitable for MLLM fine-tuning. Extensive experiments validate the effectiveness of UNIAA. UNIAA-LLaVA achieves competitive performance on all levels of UNIAA-Bench, compared with existing MLLMs. Specifically, it performs better than GPT-4V in aesthetic perception and even approaches the junior-level human. The authors find MLLMs have great potential in IAA, yet there remains plenty of room for further improvement.
Stats
The image is aesthetically pleasing, with its unique concept and well-executed composition. The color and lighting are well-balanced, with the cloudy sky providing a moody atmosphere.
Quotes
"UNIAA-LLaVA achieves competitive performance on all levels of UNIAA-Bench, compared with existing MLLMs." "UNIAA-LLaVA performs better than GPT-4V in aesthetic perception and even approaches the junior-level human."

Deeper Inquiries

How can the UNIAA framework be extended to other domains beyond image aesthetics?

The UNIAA framework can be extended to other domains beyond image aesthetics by adapting the concept of multi-modal large language models (MLLMs) to different types of data and tasks. For example, in the field of fashion, MLLMs could be trained on a dataset of fashion images and descriptions to assess the aesthetic appeal of outfits or accessories. Similarly, in interior design, MLLMs could be fine-tuned on datasets of interior spaces to evaluate the aesthetic quality of room designs. By tailoring the training data and prompts to specific domains, the UNIAA framework can be applied to a wide range of aesthetic assessment tasks.

What are the potential limitations and biases of using MLLMs for aesthetic assessment tasks?

There are several potential limitations and biases associated with using MLLMs for aesthetic assessment tasks. One limitation is the reliance on the training data, which may introduce biases based on the aesthetic preferences of the annotators or the specific characteristics of the dataset. This can lead to a lack of diversity in the aesthetic judgments made by the MLLMs. Additionally, MLLMs may struggle with understanding subtle nuances in aesthetics that require human-level intuition and creativity. They may also be limited by the quality and quantity of the training data, as well as the complexity of the aesthetic attributes being evaluated. Furthermore, MLLMs may exhibit biases based on the cultural background of the training data, potentially leading to skewed aesthetic judgments.

How can the UNIAA-Bench be further improved to better capture the nuances of human aesthetic judgment?

To better capture the nuances of human aesthetic judgment, the UNIAA-Bench can be further improved in several ways. One approach is to incorporate a wider range of aesthetic attributes and dimensions in the evaluation tasks, including more subjective and abstract concepts that are challenging for MLLMs to grasp. Additionally, introducing a feedback mechanism where human experts provide annotations and corrections to the MLLM-generated assessments can help refine the model's understanding of aesthetics over time. Moreover, conducting user studies to compare MLLM assessments with human assessments in real-world scenarios can provide valuable insights into the model's performance and areas for improvement. Finally, incorporating diverse cultural perspectives and preferences in the training data can help mitigate biases and enhance the model's ability to capture the diverse nuances of human aesthetic judgment.
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