activity
20142018
most citedCAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms

226 citations · 289 across the 12 of their papers we have counts for

collaborators
Showing 2015Show all

6 papers · 1 filter

cs.CV2015

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…

cs.CV2015★ 11 cited

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…

cs.CV2015

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…

cs.CV2015★ 13 cited

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…

cs.CV2015★ 5 cited

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…

cs.CV2015★ 16 cited

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…