Kate Saenko

46.4k citations
134 papers · 16.8k indexed · 11 hit papers · h-index 44

Kate Saenko

133 papers receiving 16.2k citations

Hit Papers

Semi-Supervised Domain Adaptation via M...387201120262016202110002.0k3.0k

Peers

Kate Saenko
Comparison fields: 5 of 186
  • Computer Vision and Pattern Recognition 11.4k
  • Artificial Intelligence 9.2k
  • Human-Computer Interaction 646
  • Signal Processing 999
  • Media Technology 604
Replace Tao Xiang with:
Tao Xiang United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Kate Saenko

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network

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.

Border = papers with Kate Saenko Line = papers co-authored together Kate Saenko links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20244
2
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization
202118
3
Universal Domain Adaptation through Self Supervision
20202
4
Federated Adversarial Domain Adaptation
202014
5
Shapeshifter Networks: Decoupling Layers from Parameters for Scalable and Effective Deep Learning
20201
6
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
202030
7
Are CNN Predictions based on Reasonable Evidence
20191
8
Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment.
20197
9
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
20199
10
Adversarial Self-Defense for Cycle-Consistent GANs
20196
11
Stable Distribution Alignment Using the Dual of the Adversarial Distance
20181
12
Text-to-Clip Video Retrieval with Early Fusion and Re-Captioning.
20188
13
A Two-Stream Variational Adversarial Network for Video Generation.
20184
14
Speaker-Follower Models for Vision-and-Language Navigation
201861
15
Hierarchical Actor-Critic.
201718
16
Learning a visuomotor controller for real world robotic grasping using simulated depth images
201750
17 201474
18
Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild
2014107
19
One-Shot Adaptation of Supervised Deep Convolutional Models
201310
20
Filtering Abstract Senses From Image Search Results
20095

About Kate Saenko

Kate Saenko is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 134 papers that have together received 16.8k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (64 papers), Domain Adaptation and Few-Shot Learning (59 papers), Advanced Image and Video Retrieval Techniques (35 papers), Human Pose and Action Recognition (31 papers), Advanced Neural Network Applications (21 papers), Speech and Audio Processing (10 papers), Video Analysis and Summarization (9 papers) and Image Retrieval and Classification Techniques (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (11.4k citations), Artificial Intelligence (9.2k citations) and Human-Computer Interaction (646 citations). Kate Saenko has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Trevor Darrell, Judy Hoffman, Eric Tzeng, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Jeff Donahue, Lisa Anne Hendricks, Baochen Sun and Jiashi Feng. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, IEEE Multimedia, IEEE Robotics and Automation Letters and The International Journal of Robotics Research.

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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