Trevor Darrell

202.1k total citations · 27 hit papers
389 papers, 78.9k citations indexed

About

Trevor Darrell is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Trevor Darrell has authored 389 papers receiving a total of 78.9k indexed citations (citations by other indexed papers that have themselves been cited), including 293 papers in Computer Vision and Pattern Recognition, 170 papers in Artificial Intelligence and 34 papers in Signal Processing. Recurrent topics in Trevor Darrell's work include Advanced Image and Video Retrieval Techniques (103 papers), Domain Adaptation and Few-Shot Learning (93 papers) and Multimodal Machine Learning Applications (81 papers). Trevor Darrell is often cited by papers focused on Advanced Image and Video Retrieval Techniques (103 papers), Domain Adaptation and Few-Shot Learning (93 papers) and Multimodal Machine Learning Applications (81 papers). Trevor Darrell collaborates with scholars based in United States, Germany and Israel. Trevor Darrell's co-authors include Jeff Donahue, Ross Girshick, Jitendra Malik, Evan Shelhamer, Jonathan Long, Kate Saenko, Sergio Guadarrama, Yangqing Jia, Judy Hoffman and Eric Tzeng and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Proceedings of the IEEE.

In The Last Decade

Trevor Darrell

377 papers receiving 75.4k citations

Hit Papers

Rich Feature Hierarchies for Accura... 1997 2026 2006 2016 2014 2016 2014 2022 2015 5.0k 10.0k 15.0k 20.0k
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. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
    2014 20.4k citations Ross Girshick, Jeff Donahue et al. profile →
  2. Fully Convolutional Networks for Semantic Segmentation
    2016 8.3k citations Evan Shelhamer, Trevor Darrell et al. profile →
  3. Caffe
    2014 7.8k citations Evan Shelhamer, Jeff Donahue et al. profile →
  4. 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 →
  5. Long-term recurrent convolutional networks for visual recognition and description
    2015 3.1k citations Jeff Donahue, Lisa Anne Hendricks et al. profile →
  6. Adversarial Discriminative Domain Adaptation
    2017 3.0k citations Eric Tzeng, Judy Hoffman et al. profile →
  7. Pfinder: real-time tracking of the human body
    1997 2.9k citations A. Azarbayejani, Trevor Darrell et al. profile →
  8. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
    2013 2.1k citations Jeff Donahue, Judy Hoffman et al. arXiv (Cornell University) profile →
  9. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
    2015 2.0k citations Ross Girshick, Jeff Donahue et al. profile →
  10. BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
    2020 1.4k citations Fisher Yu, Trevor Darrell et al. profile →
  11. Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
    2016 1.1k citations Jeff Donahue, Lisa Anne Hendricks et al. profile →
  12. The pyramid match kernel: discriminative classification with sets of image features
    2005 971 citations Trevor Darrell et al. profile →
  13. Deep Layer Aggregation
    2018 969 citations Fisher Yu, Dequan Wang et al. profile →
  14. Sequence to Sequence -- Video to Text
    2015 835 citations Trevor Darrell, Kate Saenko et al. profile →
  15. End-to-end training of deep visuomotor policies
    2016 700 citations Sergey Levine, Chelsea Finn et al. profile →
  16. Simultaneous Deep Transfer Across Domains and Tasks
    2015 690 citations Eric Tzeng, Judy Hoffman et al. profile →
  17. Learning to Hash with Binary Reconstructive Embeddings
    2009 534 citations Trevor Darrell et al. profile →
  18. Few-Shot Object Detection via Feature Reweighting
    2019 468 citations Zhuang Liu, Fisher Yu et al. profile →
  19. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
    2011 441 citations Kate Saenko, Trevor Darrell et al. profile →
  20. Semi-Supervised Domain Adaptation via Minimax Entropy
    2019 387 citations Stan Sclaroff, Trevor Darrell et al. profile →
  21. Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
    2015 351 citations Trevor Darrell et al. profile →
  22. YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition
    2013 319 citations Trevor Darrell, Kate Saenko et al. profile →
  23. PANDA: Pose Aligned Networks for Deep Attribute Modeling
    2014 317 citations Trevor Darrell et al. profile →
  24. Quasi-Dense Similarity Learning for Multiple Object Tracking
    2021 268 citations Trevor Darrell, Fisher Yu et al. profile →
  25. Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
    2021 265 citations Trevor Darrell et al. profile →
  26. Contrastive Test-Time Adaptation
    2022 136 citations Dequan Wang, Trevor Darrell et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →
  27. More Control for Free! Image Synthesis with Semantic Diffusion Guidance
    2023 106 citations Xihui Liu, Dong Huk Park 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
Trevor Darrell United States 84 54.3k 24.0k 7.5k 6.8k 5.2k 389 78.9k
Jitendra Malik United States 100 71.5k 1.3× 17.0k 0.7× 14.4k 1.9× 8.6k 1.3× 3.9k 0.8× 317 93.8k
Li Fei-Fei United States 93 69.3k 1.3× 43.3k 1.8× 7.1k 1.0× 5.3k 0.8× 7.3k 1.4× 368 106.7k
Jia Deng United States 32 40.9k 0.8× 25.2k 1.1× 5.3k 0.7× 3.5k 0.5× 6.2k 1.2× 61 65.0k
Karen Simonyan United States 23 34.0k 0.6× 21.1k 0.9× 5.6k 0.7× 3.1k 0.5× 5.6k 1.1× 49 63.3k
Alex Krizhevsky Canada 7 31.3k 0.6× 24.9k 1.0× 5.3k 0.7× 3.3k 0.5× 6.1k 1.2× 7 70.5k
Luc Van Gool Switzerland 114 62.3k 1.1× 14.5k 0.6× 9.9k 1.3× 14.7k 2.1× 2.4k 0.5× 923 78.2k
Ilya Sutskever Canada 22 36.1k 0.7× 41.1k 1.7× 5.5k 0.7× 3.8k 0.6× 6.6k 1.3× 39 95.2k
Xiaogang Wang China 120 47.1k 0.9× 14.4k 0.6× 5.0k 0.7× 5.8k 0.8× 2.4k 0.5× 1.5k 81.9k
Christian Szegedy United States 12 30.6k 0.6× 18.2k 0.8× 4.8k 0.6× 2.4k 0.4× 7.2k 1.4× 25 56.1k

Countries citing papers authored by Trevor Darrell

Since Specialization
Citations

This map shows the geographic impact of Trevor Darrell'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 Trevor Darrell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Trevor Darrell more than expected).

Fields of papers citing papers by Trevor Darrell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Trevor Darrell. 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 Trevor Darrell. The network helps show where Trevor Darrell may publish in the future.

Co-authorship network of co-authors of Trevor Darrell

This figure shows the co-authorship network connecting the top 25 collaborators of Trevor Darrell. A scholar is included among the top collaborators of Trevor Darrell 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 Trevor Darrell. Trevor Darrell 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.
Bai, Yutong, Xinyang Geng, Karttikeya Mangalam, et al.. (2024). Sequential Modeling Enables Scalable Learning for Large Vision Models. 22861–22872. 37 indexed citations
2.
Chan, David W., et al.. (2024). ALOHa: A New Measure for Hallucination in Captioning Models. 342–357. 2 indexed citations
3.
Xu, Huazhe, Yuping Luo, Shaoxiong Wang, Trevor Darrell, & Roberto Calandra. (2022). Towards Learning to Play Piano with Dexterous Hands and Touch. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 10410–10416. 20 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.
Park, Dong Huk, Samaneh Azadi, Xihui Liu, Trevor Darrell, & Anna Rohrbach. (2021). Benchmark for Compositional Text-to-Image Synthesis. Neural Information Processing Systems. 25 indexed citations
6.
Shen, Xu, et al.. (2020). ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots. IRIS UNIMORE (University of Modena and Reggio Emilia). 1170–1175. 10 indexed citations
7.
Gao, Hang, Huazhe Xu, Qi-Zhi Cai, et al.. (2019). Disentangling Propagation and Generation for Video Prediction. 9005–9014. 55 indexed citations
8.
Fried, Daniel, Ronghang Hu, Volkan Cirik, et al.. (2018). Speaker-Follower Models for Vision-and-Language Navigation. Neural Information Processing Systems. 31. 3314–3325. 61 indexed citations
9.
Yu, Fisher, Dequan Wang, Evan Shelhamer, & Trevor Darrell. (2018). Learning Rich Image Representation with Deep Layer Aggregation.. International Conference on Learning Representations. 2 indexed citations
10.
Hendricks, Lisa Anne, Oliver Wang, Eli Shechtman, et al.. (2017). Localizing Moments in Video with Natural Language. arXiv (Cornell University). 5804–5813. 39 indexed citations
11.
Tzeng, Eric, Judy Hoffman, Kate Saenko, & Trevor Darrell. (2017). Adversarial Discriminative Domain Adaptation (workshop extended abstract).. International Conference on Learning Representations. 2 indexed citations
12.
Zhu, Jun, Richard Zhang, Deepak Pathak, et al.. (2017). Toward Multimodal Image-to-Image Translation. arXiv (Cornell University). 30. 465–476. 203 indexed citations
13.
Finn, Chelsea, et al.. (2015). Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders.. arXiv (Cornell University). 16 indexed citations
14.
Girshick, Ross, Jeff Donahue, Trevor Darrell, & Jitendra Malik. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 580–587. 20422 indexed citations breakdown →
15.
Hoffman, Judy, Trevor Darrell, & Kate Saenko. (2014). Continuous Manifold Based Adaptation for Evolving Visual Domains. 867–874. 74 indexed citations
16.
Karayev, Sergey, et al.. (2012). Timely Object Recognition. MPG.PuRe (Max Planck Society). 25. 890–898. 25 indexed citations
17.
Cheng, Hui, Jingen Liu, Saad Ali, et al.. (2012). SRI-Sarnoff AURORA System at TRECVID 2012 Multimedia Event Detection and Recounting.. Journal of International Crisis and Risk Communication Research. 14 indexed citations
18.
Fritz, Mario, Kate Saenko, & Trevor Darrell. (2010). Size Matters: Metric Visual Search Constraints from Monocular Metadata. MPG.PuRe (Max Planck Society). 23. 622–630. 10 indexed citations
19.
Salzmann, Mathieu, Carl Henrik Ek, Raquel Urtasun, & Trevor Darrell. (2010). Factorized Orthogonal Latent Spaces. Bristol Research (University of Bristol). 701–708. 52 indexed citations
20.
Pentland, Alex, Irfan Essa, Trevor Darrell, A. Azarbayejani, & Stan Sclaroff. (1996). Visually guided animation. Prentice-Hall, Inc eBooks. 143–164. 1 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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