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Christoph Feichtenhofer

Christoph Feichtenhofer

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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
43
Citations
26818
World Ranking
508
National Ranking
78

Computer Science

D-Index
41
Citations
26124
World Ranking
8557
National Ranking
3656

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Christoph Feichtenhofer is affiliated with Meta Platforms, Inc. in the United States. Their research primarily focuses on computer science, with a specialization in the subfields of computer vision and pattern recognition, artificial intelligence, signal processing, electrical and electronic engineering, and radiology, nuclear medicine, and imaging.

Their work spans several main topics including:

  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Video Surveillance and Tracking Methods
  • Anomaly Detection Techniques and Applications

Feichtenhofer has contributed extensively to academic publications, with a significant number appearing in venues such as arXiv (Cornell University), the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), and the 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Their frequent publication venues also include the Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and IEEE Transactions on Pattern Analysis and Machine Intelligence.

Some recent papers authored or co-authored by Feichtenhofer include:

  • A ConvNet for the 2020s, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • TrackFormer: Multi-Object Tracking with Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • MViTv2: Improved Multiscale Vision Transformers for Classification and Detection, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Masked Feature Prediction for Self-Supervised Visual Pre-Training, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Ego4D: Around the World in 3,000 Hours of Egocentric Video, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent collaborators with whom Feichtenhofer has co-authored multiple works include Haoqi Fan, Jitendra Malik, Chao-Yuan Wu, Yanghao Li, and Karttikeya Mangalam.

Best Publications

  • A ConvNet for the 2020s

    Unknown

  • SlowFast Networks for Video Recognition

    Christoph Feichtenhofer;Haoqi Fan;Jitendra Malik;Kaiming He

  • Convolutional Two-Stream Network Fusion for Video Action Recognition

    Christoph Feichtenhofer;Axel Pinz;Andrew Zisserman

  • Spatiotemporal Multiplier Networks for Video Action Recognition

    Christoph Feichtenhofer;Axel Pinz;Richard P. Wildes

  • 3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training

    Dario Pavllo;Christoph Feichtenhofer;David Grangier;Michael Auli

  • MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

    Unknown

  • Masked Feature Prediction for Self-Supervised Visual Pre-Training

    Unknown

  • TrackFormer: Multi-Object Tracking with Transformers.

    Tim Meinhardt;Alexander Kirillov;Laura Leal-Taixé;Christoph Feichtenhofer

  • X3D: Expanding Architectures for Efficient Video Recognition

    Christoph Feichtenhofer

  • Spatiotemporal Residual Networks for Video Action Recognition

    Christoph Feichtenhofer;Axel Pinz;Richard P. Wildes

  • Detect to Track and Track to Detect

    Christoph Feichtenhofer;Axel Pinz;Andrew Zisserman

  • Masked Autoencoders As Spatiotemporal Learners

    Unknown

  • Ego4D: Around the World in 3,000 Hours of Egocentric Video

    Kristen Grauman;Andrew Westbury;Eugene Byrne;Zachary Chavis

  • Long-Term Feature Banks for Detailed Video Understanding

    Chao-Yuan Wu;Christoph Feichtenhofer;Haoqi Fan;Kaiming He

  • Convolutional Two-Stream Network Fusion for Video Action Recognition

    Christoph Feichtenhofer;Axel Pinz;Andrew Zisserman

  • VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

    Hu Xu;Gargi Ghosh;Po-Yao Huang;Dmytro Okhonko

  • VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

    Hu Xu;Gargi Ghosh;Po-Yao Huang;Dmytro Okhonko

  • Scaling Language-Image Pre-Training via Masking

    Unknown

  • Modeling Human Motion with Quaternion-Based Neural Networks

    Dario Pavllo;Christoph Feichtenhofer;Michael Auli;David Grangier

  • MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition

    Unknown

  • Masked Autoencoders that Listen

    Unknown

  • A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning

    Christoph Feichtenhofer;Haoqi Fan;Bo Xiong;Ross Girshick

  • Spatiotemporal Residual Networks for Video Action Recognition

    Christoph Feichtenhofer;Axel Pinz;Richard P. Wildes

  • Audiovisual SlowFast Networks for Video Recognition

    Fanyi Xiao;Yong Jae Lee;Kristen Grauman;Jitendra Malik

  • A Perceptual Image Sharpness Metric Based on Local Edge Gradient Analysis

    C. Feichtenhofer;H. Fassold;P. Schallauer

  • Grounded Human-Object Interaction Hotspots From Video

    Tushar Nagarajan;Christoph Feichtenhofer;Kristen Grauman

  • Temporal Residual Networks for Dynamic Scene Recognition

    Christoph Feichtenhofer;Axel Pinz;Richard P. Wildes

  • VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding

    Hu Xu;Gargi Ghosh;Po-Yao Huang;Prahal Arora

  • Ego-Topo: Environment Affordances From Egocentric Video

    Tushar Nagarajan;Yanghao Li;Christoph Feichtenhofer;Kristen Grauman

  • Multiscale Vision Transformers

    Haoqi Fan;Bo Xiong;Karttikeya Mangalam;Yanghao Li

Frequent Co-Authors

Axel Pinz
Axel Pinz Graz University of Technology
Richard P. Wildes
Richard P. Wildes York University
Jitendra Malik
Jitendra Malik University of California, Berkeley
Kristen Grauman
Kristen Grauman The University of Texas at Austin
Kaiming He
Kaiming He Facebook (United States)
Ross Girshick
Ross Girshick Facebook (United States)
Andrew Zisserman
Andrew Zisserman University of Oxford
Florian Metze
Florian Metze Carnegie Mellon University
Luke Zettlemoyer
Luke Zettlemoyer University of Washington
Lorenzo Torresani
Lorenzo Torresani Facebook (United States)

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