226 citations · 289 across the 12 of their papers we have counts for
16 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…
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu +2
Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to gene…
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…
Overlapping Cover Local Regression Machines
Mohamed Elhoseiny, Ahmed Elgammal
We present the Overlapping Domain Cover (ODC) notion for kernel machines, as a set of overlapping subsets of the data that covers the entire training set and optimized to be spatia…
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…
Write a Classifier: Predicting Visual Classifiers from Unstructured Text
Mohamed Elhoseiny, Ahmed Elgammal, Babak Saleh
People typically learn through exposure to visual concepts associated with linguistic descriptions. For instance, teaching visual object categories to children is often accompanied…
Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos
Mohamed Elhoseiny, Jingen Liu, Hui Cheng +2
We propose a new zero-shot Event Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available…
Convolutional Models for Joint Object Categorization and Pose Estimation
Mohamed Elhoseiny, Tarek El-Gaaly, Amr Bakry +1
In the task of Object Recognition, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant represe…
Sherlock: Scalable Fact Learning in Images
Mohamed Elhoseiny, Scott Cohen, Walter Chang +2
We study scalable and uniform understanding of facts in images. Existing visual recognition systems are typically modeled differently for each fact type such as objects, actions, a…
Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance
Amr Bakry, Mohamed Elhoseiny, Tarek El-Gaaly +1
This paper is focused on studying the view-manifold structure in the feature spaces implied by the different layers of Convolutional Neural Networks (CNN). There are several questi…
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…
Learning Hypergraph-regularized Attribute Predictors
Sheng Huang, Mohamed Elhoseiny, Ahmed Elgammal +1
We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. T…
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence
Mohamed Elhoseiny, Ahmed Elgammal
There has been a growing interest in mutual information measures due to their wide range of applications in Machine Learning and Computer Vision. In this paper, we present a genera…
Text to Multi-level MindMaps: A Novel Method for Hierarchical Visual Abstraction of Natural Language Text
Mohamed Elhoseiny, Ahmed Elgammal
MindMapping is a well-known technique used in note taking, which encourages learning and studying. MindMapping has been manually adopted to help present knowledge and concepts in a…