Aesthetics Quality Assessment
10 papers with code • 4 benchmarks • 6 datasets
Automatic assessment of aesthetic-related subjective ratings.
Benchmarks
These leaderboards are used to track progress in Aesthetics Quality Assessment
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AVA
AADB
Aesthetic Visual Analysis
Image Aesthetics dataset
RPCD
Latest papers with no code
Series Photo Selection via Multi-view Graph Learning
Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos.
Composition-Aware Image Aesthetics Assessment
In this work, we propose to model the image composition information as the mutual dependency of its local regions, and design a novel architecture to leverage such information to boost the performance of aesthetics assessment.
Soft Labels for Ordinal Regression
Ordinal regression attempts to solve classification problems in which categories are not independent, but rather follow a natural order.
A Constrained Deep Neural Network for Ordinal Regression
An implementation based on the CNN framework is proposed to solve the problem such that high-level features can be extracted automatically, and the optimal solution can be learned through the traditional back-propagation method.
A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input.
Composition-Preserving Deep Photo Aesthetics Assessment
Deep convolutional neural network (ConvNet) methods have recently shown promising results for aesthetics assessment.
Deep Aesthetic Quality Assessment with Semantic Information
Human beings often assess the aesthetic quality of an image coupled with the identification of the image's semantic content.
Deep Multi-Patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation
We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image.
