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20152022
most citedAttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

158 citations · 485 across the 44 of their papers we have counts for

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Showing 2016Show all

6 papers · 1 filter

cs.LG2016

Unsupervised Learning of Predictors from Unpaired Input-Output Samples

Jianshu Chen, Po-Sen Huang, Xiaodong He +2

Unsupervised learning is the most challenging problem in machine learning and especially in deep learning. Among many scenarios, we study an unsupervised learning problem of high e…

cs.CL2016

Visual Storytelling

Ting-Hao, Huang, Francis Ferraro +13

We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling. The first release of this dataset, SIND…

cs.CL2016

A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories

Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He +5

Representation and learning of commonsense knowledge is one of the foundational problems in the quest to enable deep language understanding. This issue is particularly challenging…

cs.CL2016

Character-Level Question Answering with Attention

David Golub, Xiaodong He

We show that a character-level encoder-decoder framework can be successfully applied to question answering with a structured knowledge base. We use our model for single-relation qu…

cs.CV2016

Rich Image Captioning in the Wild

Kenneth Tran, Xiaodong He, Lei Zhang +5

We present an image caption system that addresses new challenges of automatically describing images in the wild. The challenges include high quality caption quality with respect to…

cs.CL2016

Generating Natural Questions About an Image

Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin +3

There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images.…