Trevor Darrell
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Advanced Vision and Imaging
- Media Technology top 0.01%
Papers in
-
- Advanced Image and Video Retrieval Techniques 122
- Multimodal Machine Learning Applications 100
- Advanced Vision and Imaging 61
- Human Pose and Action Recognition 60
- Advanced Neural Network Applications 50
- Video Surveillance and Tracking Methods 46
- Image Retrieval and Classification Techniques 37
-
- Domain Adaptation and Few-Shot Learning 105
- Co-authors
- Jeff Donahue (14 shared papers)Ross Girshick (13 shared papers)Jitendra Malik (8 shared papers)Evan Shelhamer (10 shared papers)Jonathan Long (5 shared papers)Kate Saenko (54 shared papers)Sergio Guadarrama (11 shared papers)Yangqing Jia (16 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (17 papers)International Journal of Computer Vision (5 papers)Computer Vision and Image Understanding (4 papers)Lecture notes in computer science (36 papers)Advances in computer vision and pattern recognition (2 papers)
- Partner nations
- United StatesGermanyIsrael
In The Last Decade
Trevor Darrell
428 papers receiving 90.9k citations
Trevor Darrell's Hit Papers
Peers
Comparison fields: 5 of 223
- Computer Vision and Pattern Recognition 65.6k
- Media Technology 8.4k
- Artificial Intelligence 30.8k
- Human-Computer Interaction 3.7k
- Industrial and Manufacturing Engineering 4.0k
Countries citing papers authored by Trevor Darrell
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
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-authors
The 25 scholars most cited alongside Trevor Darrell, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 442 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Hit paper breakdown → | 2014 | 22800 |
| 2 | Fully Convolutional Networks for Semantic Segmentation Hit paper breakdown → | 2016 | 8900 |
| 3 | Caffe Hit paper breakdown → | 2014 | 8868 |
| 4 | A ConvNet for the 2020s Hit paper breakdown → | 2022 | 4421 |
| 5 | Long-term recurrent convolutional networks for visual recognition and description Hit paper breakdown → | 2015 | 3482 |
| 6 | Adversarial Discriminative Domain Adaptation Hit paper breakdown → | 2017 | 3366 |
| 7 | Pfinder: real-time tracking of the human body Hit paper breakdown → | 1997 | 3331 |
| 8 | DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition Hit paper breakdown → | 2013 | 2400 |
| 9 | Region-Based Convolutional Networks for Accurate Object Detection and Segmentation Hit paper breakdown → | 2015 | 2203 |
| 10 | Adapting Visual Category Models to New Domains Hit paper breakdown → | 2010 | 1874 |
| 11 | BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning Hit paper breakdown → | 2020 | 1577 |
| 12 | Long-Term Recurrent Convolutional Networks for Visual Recognition and Description Hit paper breakdown → | 2016 | 1178 |
| 13 | The pyramid match kernel: discriminative classification with sets of image features Hit paper breakdown → | 2005 | 1152 |
| 14 | Deep Layer Aggregation Hit paper breakdown → | 2018 | 1072 |
| 15 | Sequence to Sequence -- Video to Text Hit paper breakdown → | 2015 | 958 |
| 16 | Part-Based R-CNNs for Fine-Grained Category Detection Hit paper breakdown → | 2014 | 879 |
| 17 | Simultaneous Deep Transfer Across Domains and Tasks Hit paper breakdown → | 2015 | 818 |
| 18 | End-to-end training of deep visuomotor policies Hit paper breakdown → | 2016 | 771 |
| 19 | Learning to Hash with Binary Reconstructive Embeddings Hit paper breakdown → | 2009 | 593 |
| 20 | Few-Shot Object Detection via Feature Reweighting Hit paper breakdown → | 2019 | 538 |
About Trevor Darrell
Trevor Darrell is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Aerospace Engineering and Human-Computer Interaction, having authored 442 papers that have together received 94.8k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (122 papers), Domain Adaptation and Few-Shot Learning (105 papers), Multimodal Machine Learning Applications (100 papers), Advanced Vision and Imaging (61 papers), Human Pose and Action Recognition (60 papers), Advanced Neural Network Applications (50 papers), Video Surveillance and Tracking Methods (46 papers) and Image Retrieval and Classification Techniques (37 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (65.6k citations), Media Technology (8.4k citations), Artificial Intelligence (30.8k citations), Human-Computer Interaction (3.7k citations) and Industrial and Manufacturing Engineering (4.0k citations). Trevor Darrell has collaborated with scholars based in United States, Germany and Israel. Frequent co-authors include Jeff Donahue, Ross Girshick, Jitendra Malik, Evan Shelhamer, Jonathan Long, Kate Saenko, Sergio Guadarrama, Yangqing Jia, Judy Hoffman and Eric Tzeng. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, Computer Vision and Image Understanding, Lecture notes in computer science and Advances in computer vision and pattern recognition.
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.