Kate Saenko
Impact in
- Computer Vision and Pattern Recognition top 0.02%
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 0.02%
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Multimodal Machine Learning Applications 51
- Advanced Neural Network Applications 30
- Advanced Image and Video Retrieval Techniques 14
- Human Pose and Action Recognition 13
- Generative Adversarial Networks and Image Synthesis 7
-
- Domain Adaptation and Few-Shot Learning 53
- Explainable Artificial Intelligence (XAI) 10
- Co-authors
- Trevor Jackson Darrell (55 shared papers)Baochen Sun (8 shared papers)Judy Hoffman (17 shared papers)Marcus Rohrbach (11 shared papers)Subhashini Venugopalan (10 shared papers)Eric Tzeng (6 shared papers)Jeff Donahue (7 shared papers)Lisa Anne Hendricks (8 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (8 papers)Advances in computer vision and pattern recognition (3 papers)Lecture notes in computer science (23 papers)The International Journal of Robotics Research (2 papers)IEEE Multimedia (1 paper)
- Partner nations
- United StatesGermanyChina
In The Last Decade
Kate Saenko
171 papers receiving 30.5k citations
Kate Saenko's Hit Papers
Peers
Comparison fields: 5 of 173
- Computer Vision and Pattern Recognition 13.4k
- Artificial Intelligence 13.4k
- Control and Systems Engineering 2k
- Signal Processing 659
- Media Technology 655
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 174 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 | 3823 |
| 2 | Adversarial Discriminative Domain Adaptation Hit paper breakdown → | 2017 | 3674 |
| 3 | Deep CORAL: Correlation Alignment for Deep Domain Adaptation Hit paper breakdown → | 2016 | 2495 |
| 4 | Adapting Visual Category Models to New Domains Hit paper breakdown → | 2010 | 1995 |
| 5 | Return of Frustratingly Easy Domain Adaptation Hit paper breakdown → | 2016 | 1427 |
| 6 | Long-Term Recurrent Convolutional Networks for Visual Recognition and Description Hit paper breakdown → | 2016 | 1237 |
| 7 | Moment Matching for Multi-Source Domain Adaptation Hit paper breakdown → | 2019 | 1194 |
| 8 | Sequence to Sequence -- Video to Text Hit paper breakdown → | 2015 | 1042 |
| 9 | Simultaneous Deep Transfer Across Domains and Tasks Hit paper breakdown → | 2015 | 908 |
| 10 | Translating Videos to Natural Language Using Deep Recurrent Neural Networks Hit paper breakdown → | 2015 | 718 |
| 11 | Strong-Weak Distribution Alignment for Adaptive Object Detection Hit paper breakdown → | 2019 | 616 |
| 12 | R-C3D: Region Convolutional 3D Network for Temporal Activity Detection Hit paper breakdown → | 2017 | 599 |
| 13 | Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Hit paper breakdown → | 2016 | 551 |
| 14 | What you saw is not what you get: Domain adaptation using asymmetric kernel transforms Hit paper breakdown → | 2011 | 529 |
| 15 | Semi-Supervised Domain Adaptation via Minimax Entropy Hit paper breakdown → | 2019 | 491 |
| 16 | Grasp Pose Detection in Point Clouds Hit paper breakdown → | 2017 | 428 |
| 17 | Natural Language Object Retrieval Hit paper breakdown → | 2016 | 424 |
| 18 | YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition Hit paper breakdown → | 2013 | 391 |
| 19 | 2017 | 351 | |
| 20 | 2017 | 302 |
About Kate Saenko
Kate Saenko is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Safety Research and Control and Systems Engineering, having authored 174 papers that have together received 31.2k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (53 papers), Multimodal Machine Learning Applications (51 papers), Advanced Neural Network Applications (30 papers), Advanced Image and Video Retrieval Techniques (14 papers), Human Pose and Action Recognition (13 papers), Explainable Artificial Intelligence (XAI) (10 papers), Speech and Audio Processing (9 papers) and Generative Adversarial Networks and Image Synthesis (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (13.4k citations), Artificial Intelligence (13.4k citations), Control and Systems Engineering (2k citations), Signal Processing (659 citations) and Media Technology (655 citations). Kate Saenko has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Trevor Jackson Darrell, Baochen Sun, Judy Hoffman, Marcus Rohrbach, Subhashini Venugopalan, Eric Tzeng, Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama 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, The International Journal of Robotics Research and IEEE Multimedia.
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.