Search Results for author: Natalia Neverova

Found 22 papers, 6 papers with code

NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

no code implementations CVPR 2021 Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, Andrea Vedaldi

We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i. e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them.

XCiT: Cross-Covariance Image Transformers

4 code implementations17 Jun 2021 Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, Hervé Jegou

We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries.

Instance Segmentation Object Detection +2

Cross-Modal Retrieval Augmentation for Multi-Modal Classification

no code implementations16 Apr 2021 Shir Gur, Natalia Neverova, Chris Stauffer, Ser-Nam Lim, Douwe Kiela, Austin Reiter

Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing.

Classification Cross-Modal Retrieval +3

Training Vision Transformers for Image Retrieval

no code implementations10 Feb 2021 Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, Hervé Jégou

Transformers have shown outstanding results for natural language understanding and, more recently, for image classification.

Image Classification Image Retrieval +2

Continuous Surface Embeddings

no code implementations NeurIPS 2020 Natalia Neverova, David Novotny, Vasil Khalidov, Marc Szafraniec, Patrick Labatut, Andrea Vedaldi

In this work, we focus on the task of learning and representing dense correspondences in deformable object categories.

Pose Estimation

Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation

1 code implementation7 Apr 2020 Hanbyul Joo, Natalia Neverova, Andrea Vedaldi

While existing parametric human model fitting approaches, such as SMPLify, rely on the "view-agnostic" human pose priors to enforce the output in a plausible 3D pose space, EFT exploits the pose prior that comes from the specific 2D input observations by leveraging a fully-trained 3D pose regressor.

3D Human Pose Estimation 3D Pose Estimation

Transferring Dense Pose to Proximal Animal Classes

no code implementations CVPR 2020 Artsiom Sanakoyeu, Vasil Khalidov, Maureen S. McCarthy, Andrea Vedaldi, Natalia Neverova

Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail.

Transfer Learning

Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels

no code implementations NeurIPS 2019 Natalia Neverova, David Novotny, Andrea Vedaldi

We show that these models, by understanding uncertainty better, can solve the original DensePose task more accurately, thus setting the new state-of-the-art accuracy in this benchmark.

CoPhy: Counterfactual Learning of Physical Dynamics

no code implementations ICLR 2020 Fabien Baradel, Natalia Neverova, Julien Mille, Greg Mori, Christian Wolf

Understanding causes and effects in mechanical systems is an essential component of reasoning in the physical world.

Video Prediction

C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion

2 code implementations ICCV 2019 David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova, Andrea Vedaldi

We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images.

Structure from Motion

Dense Pose Transfer

no code implementations ECCV 2018 Natalia Neverova, Riza Alp Guler, Iasonas Kokkinos

In this work we integrate ideas from surface-based modeling with neural synthesis: we propose a combination of surface-based pose estimation and deep generative models that allows us to perform accurate pose transfer, i. e. synthesize a new image of a person based on a single image of that person and the image of a pose donor.

Pose Estimation Pose Transfer

Object Level Visual Reasoning in Videos

1 code implementation ECCV 2018 Fabien Baradel, Natalia Neverova, Christian Wolf, Julien Mille, Greg Mori

Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context.

Activity Recognition Object Detection +1

DensePose: Dense Human Pose Estimation In The Wild

21 code implementations CVPR 2018 Riza Alp Güler, Natalia Neverova, Iasonas Kokkinos

In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation.

Pose Estimation

Mass Displacement Networks

no code implementations12 Aug 2017 Natalia Neverova, Iasonas Kokkinos

Despite the large improvements in performance attained by using deep learning in computer vision, one can often further improve results with some additional post-processing that exploits the geometric nature of the underlying task.

Pose Estimation

Houdini: Fooling Deep Structured Prediction Models

no code implementations17 Jul 2017 Moustapha Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet

Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines.

General Classification Pose Estimation +3

Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning

no code implementations20 Nov 2015 Natalia Neverova, Christian Wolf, Florian Nebout, Graham Taylor

We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input.

Hand Pose Estimation

Learning Human Identity from Motion Patterns

no code implementations12 Nov 2015 Natalia Neverova, Christian Wolf, Griffin Lacey, Lex Fridman, Deepak Chandra, Brandon Barbello, Graham Taylor

We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors.

ModDrop: adaptive multi-modal gesture recognition

no code implementations31 Dec 2014 Natalia Neverova, Christian Wolf, Graham W. Taylor, Florian Nebout

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning.

Gesture Recognition

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