Learning to Look before Learning to Like: Incorporating Human Visual Cognition into Aesthetic Quality Assessment
Automated Aesthetic Quality Assessment (AQA) treats images primarily as static pixel vectors, aligning predictions with human-rating scores largely through semantic perception. However, this paradigm diverges from human aesthetic cognition, which arises from dynamic visual exploration shaped by scanning paths, processing fluency, and the interplay between bottom-up salience and top-down intention. We introduce AestheticNet, a novel cognitive-inspired AQA paradigm that integrates human-like visual cognition and semantic perception with a two-pathway architecture. The visual attention pathway, implemented as a gaze-aligned visual encoder (GAVE) pre-trained offline on eye-tracking data using...
| Authors | Liwen Yu, Chi Liu, Xiaotong Han et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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