View-Hosting: Streaming views on a single screen – Scene localization is a key component of many applications, including computer vision and image retrieval, as the goal is to identify a scene from a set of available view-aware sensors. In this work, we propose an iterative algorithm for scene localization under various camera viewpoint parameters. The proposed method is based on a low-bandwidth feature representation framework and it computes the optimal number of parameters by solving an optimization problem over the feature vectors. For this purpose, we adopt a new convolutional neural network to compute an optimal number of parameters while minimizing the cost associated with using the feature representation. Finally, we propose a deep learning model to handle the challenging scene localization problem. Experimental results on image retrieval, scene localization and object tracking show that the proposed method can be a highly promising step for scene localization.

In this work we investigate the problem of using a semantic graph model to represent texts. We first present a graph model that learns to extract semantic relationships given their data. Our approach is based on using a text graph to describe each line of text. Our model learns to produce semantic associations over pairs of text pairs, and our method is general enough to produce useful syntactic relations in a graph with semantic relationships. We show that our method is equivalent to a semantic graph search method, where a semantic tree that contains all the nodes in each category of a text is automatically constructed from the remaining ones. We also show that it is highly effective to perform semantic tree construction on the entire text.

Stacked Generative Adversarial Networks for Multi-Resolution 3D Point Clouds Regression

AIS-2: Improving, Optimizing and Estimating Multiplicity Optimization

# View-Hosting: Streaming views on a single screen

A Hierarchical Segmentation Model for 3D Action Camera Footage

Learning Graphical Models of Text to ArtifactsIn this work we investigate the problem of using a semantic graph model to represent texts. We first present a graph model that learns to extract semantic relationships given their data. Our approach is based on using a text graph to describe each line of text. Our model learns to produce semantic associations over pairs of text pairs, and our method is general enough to produce useful syntactic relations in a graph with semantic relationships. We show that our method is equivalent to a semantic graph search method, where a semantic tree that contains all the nodes in each category of a text is automatically constructed from the remaining ones. We also show that it is highly effective to perform semantic tree construction on the entire text.