SOURCE-LINKED INTELLIGENCE
Bridging Adversarial and Collaborative Learning for AI-Generated Image Quality Assessment
AI-generated image quality assessment (AIGIQA) requires jointly reasoning about perceptual fidelity and prompt alignment, two quality dimensions that are often treated as independent in existing AIGIQA models. However, by re-examining human ratings, we uncover a previously overlooked phenomenon: the two dimensions are interdependent and exhibit both competitive and cooperative interactions during human rating. This observation suggests that a unified model should neither collapse the two dimensions nor rigidly separate them, but rather adaptively negotiate their interplay. Motivated by this in
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T10:31:00.000Z
First collected: 2026-09-21T10:02:02.728Z. This is not the publication date.