226 citations · 289 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…