226 citations · 247 across the 4 of their papers we have counts for
7 papers
Large-Scale Visual Relationship Understanding
Ji Zhang, Yannis Kalantidis, Marcus Rohrbach +3
Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of <subject, relation, object> triples. In real-worl…
The Shape of Art History in the Eyes of the Machine
Ahmed Elgammal, Marian Mazzone, Bingchen Liu +2
How does the machine classify styles in art? And how does it relate to art historians' methods for analyzing style? Several studies have shown the ability of the machine to learn a…
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
Mohamed Elhoseiny, Yizhe Zhu, Han Zhang +1
In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to c…
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny +1
We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the gen…
Automatic Annotation of Structured Facts in Images
Mohamed Elhoseiny, Scott Cohen, Walter Chang +2
Motivated by the application of fact-level image understanding, we present an automatic method for data collection of structured visual facts from images with captions. Example str…
Tell and Predict: Kernel Classifier Prediction for Unseen Visual Classes from Unstructured Text Descriptions
Mohamed Elhoseiny, Ahmed Elgammal, Babak Saleh
In this paper we propose a framework for predicting kernelized classifiers in the visual domain for categories with no training images where the knowledge comes from textual descri…