Kate Saenko
Impact in
- Computer Vision and Pattern Recognition top 0.02%
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Video Analysis and Summarization
- Artificial Intelligence top 0.02%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Machine Learning and ELM
Papers in
-
- Multimodal Machine Learning Applications 78
- Advanced Image and Video Retrieval Techniques 43
- Human Pose and Action Recognition 34
- Advanced Neural Network Applications 25
- Image Retrieval and Classification Techniques 10
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- Domain Adaptation and Few-Shot Learning 72
- Topic Modeling 9
- Co-authors
- Trevor Darrell (54 shared papers)Baochen Sun (8 shared papers)Judy Hoffman (18 shared papers)Eric Tzeng (8 shared papers)Marcus Rohrbach (8 shared papers)Subhashini Venugopalan (7 shared papers)Sergio Guadarrama (10 shared papers)Jeff Donahue (8 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (6 papers)Advances in computer vision and pattern recognition (3 papers)Lecture notes in computer science (18 papers)IEEE Robotics and Automation Letters (1 paper)International Journal of Computer Vision (1 paper)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Kate Saenko
154 papers receiving 23.9k citations
Kate Saenko's Hit Papers
Peers
Comparison fields: 5 of 189
- Computer Vision and Pattern Recognition 16.3k
- Artificial Intelligence 14.4k
- Human-Computer Interaction 808
- Signal Processing 1.3k
- Media Technology 968
Countries citing papers authored by Kate Saenko
This map shows the geographic impact of Kate Saenko'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 Kate Saenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kate Saenko more than expected).
Fields of papers citing papers by Kate Saenko
This network shows the impact of papers produced by Kate Saenko. 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 Kate Saenko. The network helps show where Kate Saenko may publish in the future.
Co-authors
The 25 scholars most cited alongside Kate Saenko, 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 155 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Long-term recurrent convolutional networks for visual recognition and description Hit paper breakdown → | 2015 | 3482 |
| 2 | Adversarial Discriminative Domain Adaptation Hit paper breakdown → | 2017 | 3366 |
| 3 | Deep CORAL: Correlation Alignment for Deep Domain Adaptation Hit paper breakdown → | 2016 | 2153 |
| 4 | Adapting Visual Category Models to New Domains Hit paper breakdown → | 2010 | 1874 |
| 5 | Return of Frustratingly Easy Domain Adaptation Hit paper breakdown → | 2016 | 1299 |
| 6 | Long-Term Recurrent Convolutional Networks for Visual Recognition and Description Hit paper breakdown → | 2016 | 1178 |
| 7 | Moment Matching for Multi-Source Domain Adaptation Hit paper breakdown → | 2019 | 1072 |
| 8 | Sequence to Sequence -- Video to Text Hit paper breakdown → | 2015 | 958 |
| 9 | Simultaneous Deep Transfer Across Domains and Tasks Hit paper breakdown → | 2015 | 818 |
| 10 | What you saw is not what you get: Domain adaptation using asymmetric kernel transforms Hit paper breakdown → | 2011 | 511 |
| 11 | R-C3D: Region Convolutional 3D Network for Temporal Activity Detection Hit paper breakdown → | 2017 | 504 |
| 12 | Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Hit paper breakdown → | 2016 | 476 |
| 13 | Semi-Supervised Domain Adaptation via Minimax Entropy Hit paper breakdown → | 2019 | 435 |
| 14 | YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition Hit paper breakdown → | 2013 | 364 |
| 15 | 2017 | 283 | |
| 16 | 2017 | 271 | |
| 17 | 2017 | 265 | |
| 18 | 2020 | 212 | |
| 19 | 2019 | 210 | |
| 20 | 2015 | 210 |
About Kate Saenko
Kate Saenko is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Cancer Research and Aerospace Engineering, having authored 155 papers that have together received 24.6k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (78 papers), Domain Adaptation and Few-Shot Learning (72 papers), Advanced Image and Video Retrieval Techniques (43 papers), Human Pose and Action Recognition (34 papers), Advanced Neural Network Applications (25 papers), Image Retrieval and Classification Techniques (10 papers), Speech and Audio Processing (10 papers) and Topic Modeling (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (16.3k citations), Artificial Intelligence (14.4k citations), Human-Computer Interaction (808 citations), Signal Processing (1.3k citations) and Media Technology (968 citations). Kate Saenko has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Trevor Darrell, Baochen Sun, Judy Hoffman, Eric Tzeng, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Jeff Donahue, Lisa Anne Hendricks and Brian Kulis. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Advances in computer vision and pattern recognition, Lecture notes in computer science, IEEE Robotics and Automation Letters and International Journal of Computer Vision.
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.