Aesthetics Quality Assessment
12 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
Most implemented papers
NIMA: Neural Image Assessment
idealo/image-quality-assessment
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Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications such as evaluating image capture pipelines, storage techniques and sharing media.
Composition-Preserving Deep Photo Aesthetics Assessment
gautamMalu/Aesthetic_attributes_maps
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CVPR 2016
Deep convolutional neural network (ConvNet) methods have recently shown promising results for aesthetics assessment.
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation
Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.
Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment
Openning07/MPADA
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ACM Multimedia Conference 2018
Aggregation structures with explicit information, such as image attributes and scene semantics, are effective and popular for intelligent systems for assessing aesthetics of visual data.
Effective Aesthetics Prediction with Multi-level Spatially Pooled Features
We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database.
Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization
zhuhancheng/BLG-PIAA
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IEEE Transactions on Cybernetics 2020
Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an ``average'' user.
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
An ordinal distribution constraint is proposed to exploit the ordinal nature of regression.
Image Composition Assessment with Saliency-augmented Multi-pattern Pooling
bcmi/Image-Composition-Assessment-Dataset-CADB
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Image composition assessment is crucial in aesthetic assessment, which aims to assess the overall composition quality of a given image.
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space.
