Kate Saenko

About

Professor Kate Saenko is a faculty member at the Department of Computer Science at Boston University, where she serves as a Professor and the director of the Computer Vision and Learning Group. She is also a member of the IVC Group. She received her PhD from MIT and has held various academic and research positions, including Assistant Professor at UMass Lowell, Postdoctoral Researcher at the International Computer Science Institute, Visiting Scholar at UC Berkeley EECS, and Visiting Postdoctoral Fellow in the School of Engineering and Applied Science at Harvard University. Her research interests encompass the broad area of Artificial Intelligence, with a focus on Adaptive Machine Learning, Learning for Vision and Language Understanding, and Deep Learning.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• Mathematics
• Psychology
• Cognitive psychology
• Theoretical computer science
• Computer vision

Selected publications

• A Broader Study of Cross-Domain Few-Shot Learning

  Lecture notes in computer science · 2020 · 290 citations

  DOI

• Universal Domain Adaptation through Self Supervision

  arXiv (Cornell University) · 2020 · 163 citations

  Senior authorCorresponding

  Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap between the two domains. While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori. We propose a more universally applicable domain adaptation framework that can handle arbitrary category shift, called Domain Adaptative Neighborhood…

  DOI

• Real-Time Semantic Segmentation With Fast Attention

  IEEE Robotics and Automation Letters · 2020 · 160 citations

  In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial context (large receptive fields) and fine spatial details (high resolution), both of which incur high computational costs. In this letter, we propose a novel architecture that addresses both challenges and achieves state-of-the-art performance for semantic segmentation of high-resolution images and videos in real-time. The proposed architecture relies on our fast spatial attention, which is a simple yet effi…

  DOI

• LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrieval

  2021 · 75 citations

  The goal of weakly-supervised video moment retrieval is to localize the video segment most relevant to a description without access to temporal annotations during training. Prior work uses co-attention mechanisms to understand relationships between the vision and language data, but they lack contextual information between video frames that can be useful to determine how well a segment relates to the query. To address this, we propose an efficient Latent Graph Co-Attention Network (LoGAN) that ex…

  DOI

• MULE: Multimodal Universal Language Embedding

  Proceedings of the AAAI Conference on Artificial Intelligence · 2020 · 39 citations

  Existing vision-language methods typically support two languages at a time at most. In this paper, we present a modular approach which can easily be incorporated into existing vision-language methods in order to support many languages. We accomplish this by learning a single shared Multimodal Universal Language Embedding (MULE) which has been visually-semantically aligned across all languages. Then we learn to relate MULE to visual data as if it were a single language. Our method is not architec…

  DOI

Recent grants

• CI-NEW: Collaborative Research: COVE-Computer Vision Exchange for Data, Annotations and Tools

  NSF · $206k · 2016–2020

• EAGER: Quantifying and Reducing Data Bias in Object Detection Using Physics-based Image Synthesis

  NSF · $186k · 2014–2017

• AitF: FULL: Collaborative Research: PEARL: Perceptual Adaptive Representation Learning in the Wild

  NSF · $200k · 2015–2017

Frequent coauthors

• Trevor Darrell

  147 shared

• Bryan A. Plummer

  70 shared

• Judy Hoffman

  65 shared

• Stan Sclaroff

  Boston University

  56 shared

• Marcus Rohrbach

  43 shared

• Rogério Feris

  IBM (United States)

  42 shared

• Kuniaki Saito

  41 shared

• Rameswar Panda

  33 shared

Labs

• Machine LearningPI

Education

• Ph.D.

  MIT

• Other

  UMass Lowell

• Other

  International Computer Science Institute

• Other

  UC Berkeley EECS

• Other

  School of Engineering and Applied Science at Harvard University

Similar researchers at Boston University

• Ran Canettih-78
• Mark Crovellah-64