Regularizing Action Policies for Smooth Control with Reinforcement Learning

Siddharth Mysore, Bassel Mabsout, Renato Mancuso, Kate Saenko
IEEE International Conference on Robotics and Automation (ICRA), 2021

Paper Project Video



COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder

Kuniaki Saito, Kate Saenko, Ming-Yu Liu
European Conference on Computer Vision (ECCV), 2020 Spotlight

Paper Project Video



Learning to Scale Multilingual Representations for Vision-Language Tasks

Andrea Burns, Donghyun Kim, Derry Wijaya, Kate Saenko, Bryan A. Plummer
European Conference on Computer Vision (ECCV), 2020 Spotlight

Paper Project Video



Domain Agnostic Learning with Disentangled Representations

Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko
International Conference on Machine Learning (ICML), 2019 Long Oral

Paper Code Poster Video



Adversarial Self-Defense for Cycle-Consistent GANs

Dina Bashkirova, Ben Usman and Kate Saenko
Conference on Neural Information Processing Systems (NeurIPS), 2019

Paper Poster Code



PuppetGAN: Cross-Domain Image Manipulation by Demonstration

Ben Usman, Nick Dufour, Kate Saenko, Chris Bregler
IEEE International Conference on Computer Vision (ICCV), 2019 Oral

Paper Project



Moment Matching for Multi-Source Domain Adaptation

Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, Bo Wang
IEEE International Conference on Computer Vision (ICCV), 2019 Oral

Paper Project Dataset Chanllenge



Semi-supervised Domain Adaptation via Minimax Entropy

Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell and Kate Saenko
IEEE International Conference on Computer Vision (ICCV), 2019

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Language Features Matter: Effective Language Representations for Vision-Language Tasks

Andrea Burns, Reuben Tan, Kate Saenko, Stan Sclaroff, Bryan A. Plummer
IEEE International Conference on Computer Vision (ICCV), 2019

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Strong Weak Distribution Alignment for Adaptive Object Detection

Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019

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RISE: Randomized Input Sampling for Explanation of Black-box Models

Vitali Petsiuk, Abir Das, Kate Saenko
British Machine Vision Conference (BMVC), 2018 oral

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Syn2Real: A New Benchmark for Synthetic-to-Real Visual Domain Adaptation

Xingchao Peng, Ben Usman, Kuniaki Saito, Neela Kaushik, Judy Hoffman, Kate Saenko

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Hierarchical Actor-Critic

Andrew Levy, Robert Platt and Kate Saenko
In submission

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Text-to-Clip Video Retrieval with Early Fusion and Re-Captioning

Huijuan Xu, Kun He, Leonid Sigal, Stan Sclaroff and Kate Saenko
In submission

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Joint Event Detection and Description in Continuous Video Streams

Huijuan Xu, Boyang Li, Vasili Ramanishka, Leonid Sigal and Kate Saenko
In submission

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Adversarial Dropout Reguralization

Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada and Kate Saenko
ICLR 2018

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Stable Distribution Alignment Using the Dual of the Adversarial Distance

We investigate whether dualizing the logistic adversarial adaptation problem improves the stability of adaptation procedure and explore its connections to Maximum Mean Discrepancy.

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R-C3D: Region Convolutional 3D Network for Temporal Activity Detection

An end-to-end deep activity detection model based on 'detection by proposal' strategy.

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Explaining Captions Generated by Neural Networks

An "Explainable AI" approach that recovers spatio-temporal locations responsible for the deep captioner's predictions.

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Ask, Attend and Answer: Exploiting Question-guided Spatial Attention for Visual Question Answering

Huijuan Xu and Kate Saenko
European Conference on Computer Vision (ECCV), 2016

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Fine-to-Coarse Knowledge Transfer for Low-Res Image Classification

Xingchao Peng, Judy Hoffman, Stella X Yu, Kate Saenko
The 23rd IEEE International Conference on Image Processing, 2016 (ICIP-2016)

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Correlation Alignment for Unsupervised Domain Adaptation

Baochen Sun, Jiashi Feng, and Kate Saenko
The Thirtieth AAAI Conference on Artificial Intelligence, 2016 (AAAI-16)
Deep CORAL: Honorable Mention Paper at the TASK-CV workshop at ECCV'16
CORAL: Best Paper Prize at the TASK-CV workshop at ICCV'15

View Project CORAL Deep CORAL Book Chapter



Building ImageNet in One Day (Generating Large Scale Image Datasets from 3D CAD Models)

Baochen Sun, Xingchao Peng, and Kate Saenko
CVPR Workshop on The Future of Datasets in Vision, 2015

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VIDEO to TEXT: Automatic Natural Language Description of Video

Click below for papers, data and code.

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Learning Deep Object Detectors from 3D Models

Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko
International Conference on Computer Vision (ICCV), 2015

ICCV15 Paper ICLR15 Abstract Data



From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains

Baochen Sun and Kate Saenko
British Machine Vision Conference, 2014

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Probabilistic Color De-rendering

Y. Xiong, K. Saenko, T. Zickler, T. Darrell

Details CVPR12 Paper PAMI14 Paper View Project


Domain Adaptation for Object Recognition

Early work on visual domain adaptation for object recognition. View project for details, papers, code and datasets.

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Copyright © Kate Saenko 2017