UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images (2311.11306v2)

Abstract: With the increasing prevalence of smartphones and websites, Image Aesthetic Assessment (IAA) has become increasingly crucial. While the significance of attributes in IAA is widely recognized, many attribute-based methods lack consideration for the selection and utilization of aesthetic attributes. Our initial step involves the acquisition of aesthetic attributes from both intra- and inter-perspectives. Within the intra-perspective, we extract the direct visual attributes of images, constituting the absolute attribute. In the inter-perspective, our focus lies in modeling the relative score relationships between images within the same sequence, forming the relative attribute. Then, to better utilize image attributes in aesthetic assessment, we propose the Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both absolute and relative attributes of images. For absolute attributes, we leverage multiple absolute-attribute perception modules and an absolute-attribute interacting network. The absolute-attribute perception modules are first pre-trained on several absolute-attribute learning tasks and then used to extract corresponding absolute attribute features. The absolute-attribute interacting network adaptively learns the weight of diverse absolute-attribute features, effectively integrating them with generic aesthetic features from various absolute-attribute perspectives and generating the aesthetic prediction. To model the relative attribute of images, we consider the relative ranking and relative distance relationships between images in a Relative-Relation Loss function, which boosts the robustness of the UMAAF. Furthermore, UMAAF achieves state-of-the-art performance on TAD66K and AVA datasets, and multiple experiments demonstrate the effectiveness of each module and the model's alignment with human preference.

UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images

The paper entitled "UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images" presents a sophisticated approach to Image Aesthetic Assessment (IAA) by proposing the Unified Multi-Attribute Aesthetic Assessment Framework (UMAAF). This framework seeks to enhance IAA by integrating both absolute and relative attributes of images, aiming to capture the multilayered facets of image aesthetics.

Overview of the Framework

UMAAF excels by leveraging aesthetic attributes from intra- and inter-perspectives:

1. Absolute Attributes: These derive from inherent features within images, such as composition, color, exposure, and theme. The framework utilizes multiple Absolute-Attribute Perception Components, which are pre-trained on specific tasks associated with each attribute. These components extract concrete features from images, which are then adaptively integrated using an Absolute-Attribute Interacting Network. This network provides a sophisticated fusion of absolute features with generic aesthetic features, employing techniques such as channel attention and bilinear fusion.
2. Relative Attributes: Drawing inspiration from psychological research, which indicates that the order of image presentation affects aesthetic judgment, UMAAF introduces the Relative-Relation Loss. This novel loss function models the relative ranking and distances among images, enhancing the robustness of the aesthetic evaluation.

Quantitative Results

UMAAF demonstrates impressive numerical results, achieving state-of-the-art performance on significant datasets such as TAD66K and AVA. Notably, on the TAD66K dataset, UMAAF achieved high PLCC and SRCC scores, marking improvements over previous methods like TANet. Similarly, on the AVA dataset, UMAAF outperformed various contemporary approaches in metrics such as PLCC and reduced EMD, indicating strong alignment with human aesthetic judgments.

Implications and Future Directions

The UMAAF framework has notable theoretical and practical implications:

• Theoretical: By employing a comprehensive system that integrates multifaceted attributes, UMAAF advances the understanding of how different elements contribute to image aesthetics. This could spur further investigation into attribute interactions and other aspects of human visual perception.
• Practical: The integration of absolute and relative attributes in aesthetic evaluation presents opportunities for applications such as automated image enhancement, content-based image retrieval, and photo curation.

Speculation on Future Developments

The UMAAF framework opens avenues for expanding AI capabilities in visual aesthetics. Future research could leverage this framework to explore:

• Attribute Selection: Investigating additional aesthetic attributes that may contribute to nuanced aesthetic judgments.
• Model Interpretability: Enhancing transparency in AI decisions, potentially leading to models that can justify their aesthetic evaluations.

With its innovative approach and comprehensive results, UMAAF stands as a significant contribution to the ongoing development of image aesthetic assessment methodologies. It paves the way for more nuanced and human-aligned aesthetic judgments in AI systems.