activity
20102023
most citedShow, Attend and Tell: Neural Image Caption Generation with Visual Attention

7.4k citations · 24.2k across the 136 of their papers we have counts for

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Showing 2016Show all

18 papers · 1 filter

stat.ML2016★ 4 cited

Generalizable Features From Unsupervised Learning

Mehdi Mirza, Aaron Courville, Yoshua Bengio

Humans learn a predictive model of the world and use this model to reason about future events and the consequences of actions. In contrast to most machine predictors, we exhibit an…

cs.CV2016★ 5 cited

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

David Vázquez, Jorge Bernal, F. Javier Sánchez +5

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for…

cs.SD2016★ 362 cited

SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

Soroush Mehri, Kundan Kumar, Ishaan Gulrajani +5

In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memor…

cs.LG2016★ 81 cited

PixelVAE: A Latent Variable Model for Natural Images

Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed +4

Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but ha…

cs.AI2016★ 19 cited

GuessWhat?! Visual object discovery through multi-modal dialogue

Harm de Vries, Florian Strub, Sarath Chandar +3

We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The goal of the game is to locate an unknow…

cs.CV2016★ 6 cited

A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering

Tegan Maharaj, Nicolas Ballas, Anna Rohrbach +2

While deep convolutional neural networks frequently approach or exceed human-level performance at benchmark tasks involving static images, extending this success to moving images i…