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85 Results

December 15, 2017

Mapping the world population one building at a time

ArXive

Here, we present a computer vision method based on machine learning to create population maps from satellite imagery at a global scale, with a spatial sensitivity corresponding to individual buildings and suitable for global deployment.

By: Tobias Tiecke, Xianming Liu, Amy Zhang, Andreas Gros, Nan Li, Gregory Yetman, Talip Kilic, Siobhan Murray
July 21, 2017

Link the head to the “beak”: Zero Shot Learning from Noisy Text Description at Part Precision

CVPR 2017

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 connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. F

By: Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, Ahmed Elgammal
July 21, 2017

Relationship Proposal Networks

Conference on Computer Vision and Pattern Recognition 2017

In this paper we address the challenges of image scene object recognition by using pairs of related regions in images to train a relationship proposer that at test time produces a manageable number of related regions.

By: Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang, Ahmed Elgammal
May 21, 2017

CAN: Creative Adversarial Networks

IEEE International Conference on Communications (ICCC)

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 generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution.

By: Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone
May 16, 2017

Cultural Diffusion and Trends in Facebook Photographs

The International AAAI Conference on Web and Social Media (ICWSM)

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, i.e., photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs.

By: Quenzeng You, Dario Garcia, Manohar Paluri, Jiebo Luo, Jungseock Joo
April 19, 2017

Joint User-Entity Representation Learning for Event Recommendation in Social Network

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

In this work, we consider the heavy sparseness in both user and event feedback history caused by short lifespans (transiency) of events and user participation patterns in a production event system. We propose to solve the resulting cold-start problems by introducing a joint representation model to project users and events into the same latent space.

By: Lijun Tang, Eric Yi Liu
April 3, 2017

Detecting Large Reshare Cascades in Social Networks

International Conference on World Wide Web

In this paper, we propose SansNet, a network agnostic approach towards detecting large reshare cascades in online social networks.

By: Karthik Subbian, B. Aditya Prakash, Lada Adamic
November 1, 2016

Neural Text Generation from Structured Data with Application to the Biography Domain

Empirical Methods in Natural Language Processing (EMNLP)

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.

By: Remi Lebret, David Grangier, Michael Auli
October 28, 2016

Bilingual Methods for Adaptive Training Data Selection for Machine Translation

Association for the Machine Translation in Americas

We propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus.us.

By: Boxing Chen, Roland Kuhn, George Foster, Colin Cherry, Fei Huang
October 10, 2016

Learning to Refine Object Segments

European Conference on Computer Vision

In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.

By: Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollar