Search Results for author: Maxime Oquab

Found 11 papers, 3 papers with code

Efficient conditioned face animation using frontally-viewed embedding

no code implementations16 Mar 2022 Maxime Oquab, Daniel Haziza, Ludovic Schwartz, Tao Xu, Katayoun Zand, Rui Wang, Peirong Liu, Camille Couprie

As the quality of few shot facial animation from landmarks increases, new applications become possible, such as ultra low bandwidth video chat compression with a high degree of realism.

Self-appearance-aided Differential Evolution for Motion Transfer

no code implementations9 Oct 2021 Peirong Liu, Rui Wang, Xuefei Cao, Yipin Zhou, Ashish Shah, Maxime Oquab, Camille Couprie, Ser-Nam Lim

Image animation transfers the motion of a driving video to a static object in a source image, while keeping the source identity unchanged.

Image Animation

Can RNNs learn Recursive Nested Subject-Verb Agreements?

no code implementations6 Jan 2021 Yair Lakretz, Théo Desbordes, Jean-Rémi King, Benoît Crabbé, Maxime Oquab, Stanislas Dehaene

Finally, probing the internal states of the model during the processing of sentences with nested tree structures, we found a complex encoding of grammatical agreement information (e. g. grammatical number), in which all the information for multiple words nouns was carried by a single unit.

Low Bandwidth Video-Chat Compression using Deep Generative Models

no code implementations1 Dec 2020 Maxime Oquab, Pierre Stock, Oran Gafni, Daniel Haziza, Tao Xu, Peizhao Zhang, Onur Celebi, Yana Hasson, Patrick Labatut, Bobo Bose-Kolanu, Thibault Peyronel, Camille Couprie

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network.

Measuring causal influence with back-to-back regression: the linear case

no code implementations25 Sep 2019 Jean-Remi King, Francois Charton, Maxime Oquab, David Lopez-Paz

Identifying causes from observations can be particularly challenging when i) potential factors are difficult to manipulate individually and ii) observations are complex and multi-dimensional.

Causal Identification

Learning about an exponential amount of conditional distributions

1 code implementation NeurIPS 2019 Mohamed Ishmael Belghazi, Maxime Oquab, Yann Lecun, David Lopez-Paz

We introduce the Neural Conditioner (NC), a self-supervised machine able to learn about all the conditional distributions of a random vector $X$.

General Classification

Geometrical Insights for Implicit Generative Modeling

no code implementations21 Dec 2017 Leon Bottou, Martin Arjovsky, David Lopez-Paz, Maxime Oquab

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion.

Revisiting Classifier Two-Sample Tests

1 code implementation20 Oct 2016 David Lopez-Paz, Maxime Oquab

The goal of this paper is to establish the properties, performance, and uses of C2ST.

Causal Discovery

Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks

no code implementations CVPR 2014 Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic

We show that despite differences in image statistics and tasks in the two datasets, the transferred representation leads to significantly improved results for object and action classification, outperforming the current state of the art on Pascal VOC 2007 and 2012 datasets.

Action Classification Action Localization +4

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