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

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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