5.6k citations · 16.3k across the 53 of their papers we have counts for
20 papers · 1 filter
Stochastic Video Generation with a Learned Prior
Remi Denton, Rob Fergus
Generating video frames that accurately predict future world states is challenging. Existing approaches either fail to capture the full distribution of outcomes, or yield blurry ge…
Learning by Asking Questions
Ishan Misra, Ross Girshick, Rob Fergus +3
We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visu…
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
Remi Denton, Sam Gross, Rob Fergus
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator w…
Simple Baseline for Visual Question Answering
Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar +2
We describe a very simple bag-of-words baseline for visual question answering. This baseline concatenates the word features from the question and CNN features from the image to pre…
Deep End2End Voxel2Voxel Prediction
Du Tran, Lubomir Bourdev, Rob Fergus +2
Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis. However, so far their most successful applications have been…
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Emily Denton, Soumith Chintala, Arthur Szlam +1
In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a…