Beauty Is in the Eye of the Beholder

Abstract

Aesthetics is a subjective concept that is likely to be perceived differently among people of different ages, genders, and cultural backgrounds. While techniques that directly compute this concept in images has seen increasing attention by the multimedia and machine-learning community, there are very few attempts at encoding the influences from the photographer’s viewpoint. This work demonstrates how the aesthetic quality of photos can be better learned by accounting for the demographic background of a photographer. A new AVA-PD (Photographer Demographic) dataset is created to supplement the AVA dataset by providing photographers’ age, gender and location attributes. Two deep convolutional neural network (CNN) architectures are proposed to utilize demographic information for aesthetic prediction of photos; both are shown to yield better prediction capabilities compared to most existing approaches. By leveraging on AVA-PD meta-data, we also present some additional machine-learnable tasks such as identifying the photographer and predicting photography styles from a person’s gallery of photos.

More by Magzhan Kairanbay

• End-to-End VITS Outperforms Two-Stage Tacotron 2 Synthesis for Kazakh: A Comparative Study on a New Multi-Domain Speech CorpusAlgorithms · 2026
• Vision-Language Temporal Analysis for Illegal Waste Dumping Detection in Surveillance VideosIEEE/CVF Winter Conference on Applications of Computer Vision Workshops · 2026 · Citations: 2
• Real-Time Multi-Parameter Petroleum Coke Quality Monitoring Using Computer Vision and Integrated Anomaly Detection for Aluminum Smelting OperationsInternational Conference on Computer and Applications · 2025
• Multi-Dimensional Tail Classification in Procurement Spend Analysis: A Data-Driven Approach to Strategic Sourcing OptimizationInternational Conference on Computer and Applications · 2025