• Publications
  • Influence
Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions
TLDR
An approach for zero-shot learning of object categories where the description of unseen categories comes in the form of typical text such as an encyclopedia entry, without the need to explicitly defined attributes is proposed. Expand
Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
TLDR
A machine is developed that is able to make aesthetic-related semantic-level judgments, such as predicting a painting's style, genre, and artist, as well as providing similarity measures optimized based on the knowledge available in the domain of art historical interpretation. Expand
Learning style similarity for searching infographics
TLDR
A method for measuring the style similarity between infographics is presented and it is found that a combination of color histograms and Histograms-of-Gradients (HoG) features is most effective in characterizing the style of infographics. Expand
Object-Centric Anomaly Detection by Attribute-Based Reasoning
TLDR
This paper introduces the abnormality detection as a recognition problem and shows how to model typicalities and, consequently, meaningful deviations from prototypical properties of categories. Expand
Write a Classifier: Predicting Visual Classifiers from Unstructured Text
TLDR
This work proposes and investigates two baseline formulations, based on regression and domain transfer, that predict a linear classifier and proposes a new constrained optimization formulation that combines a regression function and a knowledge transfer function with additional constraints to predict the parameters of alinear classifier. Expand
Quantifying Creativity in Art Networks
TLDR
This paper proposes a novel computational framework for assessing the creativity of creative products, such as paintings, sculptures, poetry, etc, based on constructing a network between creative products and using this network to infer about the originality and influence of its nodes. Expand
Toward automated discovery of artistic influence
TLDR
A comparative study of different classification methodologies for the task of fine-art style classification and a visualization of artists based on the similarity between their works, as well as investigating the question “Who influenced this artist?” by looking at his masterpieces and comparing them to others. Expand
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks
TLDR
This work proposes computational models to improve the generalization capacity of CNNs by considering how typical a training image looks like, and shows that involving a typicality measure can improve the classification results on a new set of images by a large margin. Expand
An Early Framework for Determining Artistic Influence
TLDR
The question "Who influenced this artist?" is answered by looking at his masterpieces and comparing them to others by proposing the interesting problem of automatic influence determination between painters which has not been explored well. Expand
A Unified Framework for Painting Classification
  • B. Saleh, A. Elgammal
  • Computer Science
  • IEEE International Conference on Data Mining…
  • 14 November 2015
TLDR
A machine is developed that is able to make aesthetic-related semantic-level judgments, such as predicting a painting's style, genre, and artist, as well as providing similarity measures optimized based on the knowledge available in the domain of art historical interpretation. Expand
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