Art Analysis
10 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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SemArt
HappyDB
Latest papers with no code
Generative AI in the Construction Industry: A State-of-the-art Analysis
The construction industry is a vital sector of the global economy, but it faces many productivity challenges in various processes, such as design, planning, procurement, inspection, and maintenance.
AI Hazard Management: A framework for the systematic management of root causes for AI risks
In addition, to ensure the AI system's auditability, the proposed framework systematically documents evidence that the potential impact of identified AI hazards could be reduced to a tolerable level.
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques
We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than others.
Representing and extracting knowledge from single cell data
Single-cell analysis is currently one of the most high-resolution techniques to study biology.
Deep neural network techniques for monaural speech enhancement: state of the art analysis
We also review the use of speech-enhancement pre-trained models to boost speech enhancement process.
Automatic Text Summarization Methods: A Comprehensive Review
One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading.
Optimal Order Simple Regret for Gaussian Process Bandits
Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$.
A deep learning approach to clustering visual arts
The method uses a pre-trained convolutional network to extract features and then feeds these features into a deep embedded clustering model, where the task of mapping the input data to a latent space is jointly optimized with the task of finding a set of cluster centroids in this latent space.
Integrating Contextual Knowledge to Visual Features for Fine Art Classification
Automatic art analysis has seen an ever-increasing interest from the pattern recognition and computer vision community.
Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings
We propose ArtSAGENet, a novel multimodal architecture that integrates Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), to jointly learn visual and semantic-based artistic representations.
