Christoph Feichtenhofer

21.3k total citations · 9 hit papers
34 papers, 10.6k citations indexed

About

Christoph Feichtenhofer is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Christoph Feichtenhofer has authored 34 papers receiving a total of 10.6k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 19 papers in Artificial Intelligence and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Christoph Feichtenhofer's work include Human Pose and Action Recognition (19 papers), Multimodal Machine Learning Applications (14 papers) and Anomaly Detection Techniques and Applications (9 papers). Christoph Feichtenhofer is often cited by papers focused on Human Pose and Action Recognition (19 papers), Multimodal Machine Learning Applications (14 papers) and Anomaly Detection Techniques and Applications (9 papers). Christoph Feichtenhofer collaborates with scholars based in United States, Austria and Israel. Christoph Feichtenhofer's co-authors include Chao-Yuan Wu, Axel Pinz, Haoqi Fan, Saining Xie, Trevor Darrell, Zhuang Liu, Hanzi Mao, Kaiming He, Andrew Zisserman and Jitendra Malik and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and IEEE Signal Processing Letters.

In The Last Decade

Christoph Feichtenhofer

34 papers receiving 10.4k citations

Hit Papers

A ConvNet for the 2020s 2016 2026 2019 2022 2022 2019 2016 2022 2017 1000 2.0k 3.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A ConvNet for the 2020s
    2022 3.7k citations Zhuang Liu, Hanzi Mao et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →
  2. SlowFast Networks for Video Recognition
    2019 2.0k citations Christoph Feichtenhofer, Haoqi Fan et al. profile →
  3. Convolutional Two-Stream Network Fusion for Video Action Recognition
    2016 1.8k citations Christoph Feichtenhofer, Axel Pinz et al. profile →
  4. TrackFormer: Multi-Object Tracking with Transformers
    2022 524 citations Tim Meinhardt, Alexander Kirillov et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →
  5. Spatiotemporal Multiplier Networks for Video Action Recognition
    2017 427 citations Christoph Feichtenhofer, Axel Pinz et al. profile →
  6. MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
    2022 424 citations Yanghao Li, Chao-Yuan Wu et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →
  7. Masked Feature Prediction for Self-Supervised Visual Pre-Training
    2022 324 citations Haoqi Fan, Saining Xie et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →
  8. VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
    2021 247 citations Hu Xu, Gargi Ghosh et al. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing profile →
  9. Scaling Language-Image Pre-Training via Masking
    2023 112 citations Yanghao Li, Haoqi Fan et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Christoph Feichtenhofer United States 23 7.7k 4.2k 1.4k 697 691 34 10.6k
Haoqi Fan United States 19 7.3k 1.0× 6.0k 1.5× 926 0.7× 784 1.1× 417 0.6× 30 11.4k
Sergio Guadarrama United States 18 9.7k 1.3× 4.6k 1.1× 1.2k 0.9× 939 1.3× 636 0.9× 51 13.7k
Weiming Hu China 51 8.8k 1.1× 3.0k 0.7× 1.2k 0.9× 701 1.0× 592 0.9× 306 11.5k
Liang Lin China 66 11.9k 1.5× 4.8k 1.2× 1.1k 0.8× 1.5k 2.2× 352 0.5× 432 15.9k
Bin Xiao China 15 5.7k 0.7× 2.0k 0.5× 985 0.7× 969 1.4× 742 1.1× 57 8.2k
Dahua Lin Hong Kong 50 11.8k 1.5× 6.0k 1.5× 2.0k 1.4× 929 1.3× 1.0k 1.5× 201 15.8k
Kate Saenko United States 44 11.4k 1.5× 9.2k 2.2× 1.1k 0.8× 604 0.9× 646 0.9× 134 16.8k
Hanqing Lu China 47 11.6k 1.5× 4.7k 1.1× 1.9k 1.4× 1.8k 2.6× 1.2k 1.7× 314 14.9k
Thomas B. Moeslund Denmark 41 6.5k 0.8× 1.7k 0.4× 1.6k 1.1× 859 1.2× 1.4k 2.0× 328 10.0k

Countries citing papers authored by Christoph Feichtenhofer

Since Specialization
Citations

This map shows the geographic impact of Christoph Feichtenhofer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Christoph Feichtenhofer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christoph Feichtenhofer more than expected).

Fields of papers citing papers by Christoph Feichtenhofer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christoph Feichtenhofer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Christoph Feichtenhofer. The network helps show where Christoph Feichtenhofer may publish in the future.

Co-authorship network of co-authors of Christoph Feichtenhofer

This figure shows the co-authorship network connecting the top 25 collaborators of Christoph Feichtenhofer. A scholar is included among the top collaborators of Christoph Feichtenhofer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Christoph Feichtenhofer. Christoph Feichtenhofer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Rajasegaran, Jathushan, Georgios Pavlakos, Angjoo Kanazawa, Christoph Feichtenhofer, & Jitendra Malik. (2023). On the Benefits of 3D Pose and Tracking for Human Action Recognition. 640–649. 19 indexed citations
2.
Singh, Mannat, Quentin Duval, Kalyan Vasudev Alwala, et al.. (2023). The effectiveness of MAE pre-pretraining for billion-scale pretraining. 5461–5471. 14 indexed citations
3.
Hu, Xu, Saining Xie, Po-Yao Huang, et al.. (2023). CiT: Curation in Training for Effective Vision-Language Data. 15134–15143. 7 indexed citations
4.
Liu, Zhuang, Hanzi Mao, Chao-Yuan Wu, et al.. (2022). A ConvNet for the 2020s. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11966–11976. 3721 indexed citations breakdown →
5.
Meinhardt, Tim, Alexander Kirillov, Laura Leal-Taixé, & Christoph Feichtenhofer. (2022). TrackFormer: Multi-Object Tracking with Transformers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 8834–8844. 524 indexed citations breakdown →
6.
Li, Yanghao, Chao-Yuan Wu, Haoqi Fan, et al.. (2022). MViTv2: Improved Multiscale Vision Transformers for Classification and Detection. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4794–4804. 424 indexed citations breakdown →
7.
Wu, Chao-Yuan, Yanghao Li, Karttikeya Mangalam, et al.. (2022). MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 13577–13587. 94 indexed citations
8.
Xiong, Bo, Haoqi Fan, Kristen Grauman, & Christoph Feichtenhofer. (2021). Multiview Pseudo-Labeling for Semi-supervised Learning from Video. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 7189–7199. 39 indexed citations
9.
Xu, Hu, Gargi Ghosh, Po-Yao Huang, et al.. (2021). VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6787–6800. 247 indexed citations breakdown →
10.
Xu, Hu, Gargi Ghosh, Po-Yao Huang, et al.. (2021). VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding. 4227–4239. 56 indexed citations
11.
Fan, Haoqi, Tullie Murrell, Heng Wang, et al.. (2021). PyTorchVideo. 3783–3786. 28 indexed citations
12.
Feichtenhofer, Christoph, Haoqi Fan, Bo Xiong, Ross Girshick, & Kaiming He. (2021). A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning. 3298–3308. 136 indexed citations
13.
Wu, Chao-Yuan, Ross Girshick, Kaiming He, Christoph Feichtenhofer, & Philipp Krähenbühl. (2020). A Multigrid Method for Efficiently Training Video Models. 150–159. 50 indexed citations
14.
Feichtenhofer, Christoph, Haoqi Fan, Jitendra Malik, & Kaiming He. (2019). SlowFast Networks for Video Recognition. 6201–6210. 2000 indexed citations breakdown →
15.
16.
Wu, Chao-Yuan, Christoph Feichtenhofer, Haoqi Fan, et al.. (2019). Long-Term Feature Banks for Detailed Video Understanding. 284–293. 242 indexed citations
17.
Feichtenhofer, Christoph, Axel Pinz, Richard P. Wildes, & Andrew Zisserman. (2019). Deep Insights into Convolutional Networks for Video Recognition. International Journal of Computer Vision. 128(2). 420–437. 15 indexed citations
18.
Feichtenhofer, Christoph, Axel Pinz, & Richard P. Wildes. (2017). Temporal Residual Networks for Dynamic Scene Recognition. 7435–7444. 51 indexed citations
19.
Feichtenhofer, Christoph, Axel Pinz, & Andrew Zisserman. (2016). Convolutional Two-Stream Network Fusion for Video Action Recognition. 1933–1941. 1767 indexed citations breakdown →
20.
Feichtenhofer, Christoph, Axel Pinz, & Richard P. Wildes. (2016). Dynamic Scene Recognition with Complementary Spatiotemporal Features. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(12). 2389–2401. 19 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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