Christoph Feichtenhofer

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TrackFormer: Multi-Object Tracking with Transformers

We present TrackFormer, an end-to-end multi-object tracking and segmenta...
01/07/2021 ∙ by Tim Meinhardt, et al. ∙ 16 ∙ share

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X3D: Expanding Architectures for Efficient Video Recognition

This paper presents X3D, a family of efficient video networks that progr...
04/09/2020 ∙ by Christoph Feichtenhofer, et al. ∙ 0 ∙ share

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Feature Pyramid Grids

Feature pyramid networks have been widely adopted in the object detectio...
04/07/2020 ∙ by Kai Chen, et al. ∙ 5 ∙ share

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Audiovisual SlowFast Networks for Video Recognition

We present Audiovisual SlowFast Networks, an architecture for integrated...
01/23/2020 ∙ by Fanyi Xiao, et al. ∙ 23 ∙ share

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EGO-TOPO: Environment Affordances from Egocentric Video

First-person video naturally brings the use of a physical environment to...
01/14/2020 ∙ by Tushar Nagarajan, et al. ∙ 13 ∙ share

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A Multigrid Method for Efficiently Training Video Models

Training competitive deep video models is an order of magnitude slower t...
12/02/2019 ∙ by Chao-Yuan Wu, et al. ∙ 7 ∙ share

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Learning Temporal Pose Estimation from Sparsely-Labeled Videos

Modern approaches for multi-person pose estimation in video require larg...
06/06/2019 ∙ by Gedas Bertasius, et al. ∙ 0 ∙ share

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Grounded Human-Object Interaction Hotspots from Video (Extended Abstract)

Learning how to interact with objects is an important step towards embod...
06/03/2019 ∙ by Tushar Nagarajan, et al. ∙ 0 ∙ share

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Modeling Human Motion with Quaternion-based Neural Networks

Previous work on predicting or generating 3D human pose sequences regres...
01/21/2019 ∙ by Dario Pavllo, et al. ∙ 0 ∙ share

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Long-Term Feature Banks for Detailed Video Understanding

To understand the world, we humans constantly need to relate the present...
12/12/2018 ∙ by Chao-Yuan Wu, et al. ∙ 4 ∙ share

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Grounded Human-Object Interaction Hotspots from Video

Learning how to interact with objects is an important step towards embod...
12/11/2018 ∙ by Tushar Nagarajan, et al. ∙ 6 ∙ share

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Learning Discriminative Motion Features Through Detection

Despite huge success in the image domain, modern detection models such a...
12/11/2018 ∙ by Gedas Bertasius, et al. ∙ 0 ∙ share

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SlowFast Networks for Video Recognition

We present SlowFast networks for video recognition. Our model involves (...
12/10/2018 ∙ by Christoph Feichtenhofer, et al. ∙ 0 ∙ share

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3D human pose estimation in video with temporal convolutions and semi-supervised training

In this work, we demonstrate that 3D poses in video can be effectively e...
11/28/2018 ∙ by Dario Pavllo, et al. ∙ 0 ∙ share

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Camera-based vehicle velocity estimation from monocular video

This paper documents the winning entry at the CVPR2017 vehicle velocity ...
02/20/2018 ∙ by Moritz Kampelmühler, et al. ∙ 0 ∙ share

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What have we learned from deep representations for action recognition?

As the success of deep models has led to their deployment in all areas o...
01/04/2018 ∙ by Christoph Feichtenhofer, et al. ∙ 0 ∙ share

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Detect to Track and Track to Detect

Recent approaches for high accuracy detection and tracking of object cat...
10/11/2017 ∙ by Christoph Feichtenhofer, et al. ∙ 0 ∙ share

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Spatiotemporal Residual Networks for Video Action Recognition

Two-stream Convolutional Networks (ConvNets) have shown strong performan...
11/07/2016 ∙ by Christoph Feichtenhofer, et al. ∙ 0 ∙ share

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Convolutional Two-Stream Network Fusion for Video Action Recognition

Recent applications of Convolutional Neural Networks (ConvNets) for huma...
04/22/2016 ∙ by Christoph Feichtenhofer, et al. ∙ 0 ∙ share

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Christoph Feichtenhofer

Featured Co-authors

Andrew Zisserman

Chen Change Loy

Jitendra Malik

Dahua Lin

Kai Chen

Kristen Grauman

Ross Girshick

Michael Auli

Kaiming He

Lorenzo Torresani

Laura Leal-Taixé

TrackFormer: Multi-Object Tracking with Transformers

X3D: Expanding Architectures for Efficient Video Recognition

Feature Pyramid Grids

Audiovisual SlowFast Networks for Video Recognition

EGO-TOPO: Environment Affordances from Egocentric Video

A Multigrid Method for Efficiently Training Video Models

Learning Temporal Pose Estimation from Sparsely-Labeled Videos

Grounded Human-Object Interaction Hotspots from Video (Extended Abstract)

Modeling Human Motion with Quaternion-based Neural Networks

Long-Term Feature Banks for Detailed Video Understanding

Grounded Human-Object Interaction Hotspots from Video

Learning Discriminative Motion Features Through Detection

SlowFast Networks for Video Recognition

3D human pose estimation in video with temporal convolutions and semi-supervised training

Camera-based vehicle velocity estimation from monocular video

What have we learned from deep representations for action recognition?

Detect to Track and Track to Detect

Spatiotemporal Residual Networks for Video Action Recognition

Convolutional Two-Stream Network Fusion for Video Action Recognition