Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts

• URL: http://arxiv.org/abs/2503.06678v1
• Date: Sun, 09 Mar 2025 16:07:58 GMT
• Title: Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts
• Authors: Hantao Zhou, Rui Yang, Longxiang Tang, Guanyi Qin, Yan Zhang, Runze Hu, Xiu Li,
• Abstract summary: textbfGamma, a textbfGeneric imtextbfAge assesstextbfMent model, can effectively assess images from diverse scenes through mixed-dataset training.<n>Our Gamma model is trained and evaluated on 12 datasets spanning 6 image assessment scenarios.
• Score: 23.48816491333345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image assessment aims to evaluate the quality and aesthetics of images and has been applied across various scenarios, such as natural and AIGC scenes. Existing methods mostly address these sub-tasks or scenes individually. While some works attempt to develop unified image assessment models, they have struggled to achieve satisfactory performance or cover a broad spectrum of assessment scenarios. In this paper, we present \textbf{Gamma}, a \textbf{G}eneric im\textbf{A}ge assess\textbf{M}ent model using \textbf{M}ixture of \textbf{A}ssessment Experts, which can effectively assess images from diverse scenes through mixed-dataset training. Achieving unified training in image assessment presents significant challenges due to annotation biases across different datasets. To address this issue, we first propose a Mixture of Assessment Experts (MoAE) module, which employs shared and adaptive experts to dynamically learn common and specific knowledge for different datasets, respectively. In addition, we introduce a Scene-based Differential Prompt (SDP) strategy, which uses scene-specific prompts to provide prior knowledge and guidance during the learning process, further boosting adaptation for various scenes. Our Gamma model is trained and evaluated on 12 datasets spanning 6 image assessment scenarios. Extensive experiments show that our unified Gamma outperforms other state-of-the-art mixed-training methods by significant margins while covering more scenes. Code: https://github.com/zht8506/Gamma.

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