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<h2 class="page-header">News</h2>
<ul>
<li> <a href="https://drive.google.com/open?id=1s_5AwOHfRCWvHesWBfCUkbEJ8dqBmNdR"> Slides (PDF) </a>
from Kate Saenko's talk at the
<a href="https://sites.google.com/view/learning-with-limited-data/schedule?authuser=0">
ICCV 2019 Tutorial on Learning with Limited Labels</a>
</li>
<li> Our dataset<a href="http://ai.bu.edu/DomainNet/"> DomainNet </a> has now been released. The dataset contains 6 domains, 345 categories and about 0.6 million images, which is by far the largest cross-domain dataset.</li>
<li>Our paper <a href="http://proceedings.mlr.press/v97/peng19b/peng19b.pdf">Domain Agnostic Learning with Disentangled Representations</a> was accepted to ICML2019 as a <b>Long Oral</b> paper</li>
<li>Our paper <a href="https://arxiv.org/pdf/1812.01754.pdf">Moment Matching for Multi-Source Domain Adaptation</a> was accepted to ICCV2019 as an <b>Oral</b> paper</li>
<li> Our paper <a href="https://arxiv.org/pdf/1812.04798.pdf">Strong-Weak Distribution Alignment for Adaptive Object Detection</a> was accepted to CVPR 2019</li>
<li> Several new papers on uncovering bias in captioning models (<a href="https://arxiv.org/abs/1807.00517">ECCV'18</a>,
<a href="https://arxiv.org/abs/1809.02156">EMNLP'18</a>), explainable AI (<a href="https://arxiv.org/abs/1806.07421">BMVC'18</a>,
<a href="https://arxiv.org/abs/1807.08556">ECCV'18</a>) and language-based navigation (<a href=""https://arxiv.org/abs/1806.02724">NIPS'18)</a>
</li>
<li> Introducing High School Women to the World of Artificial Intelligence <br>
<a href="http://www.bu.edu/today/2018/boston-university-ai4all/">
</li>
</li>
<li> Our paper <a href="https://openreview.net/pdf?id=HJIoJWZCZ"> Adversarial Dropout Regularization </a> was accepted to ICLR 2018.
<li> Code of the ICCV 2017 paper <a href="./r-c3d/index.html"> R-C3D for action detection in video</a> is released in the group's <a href="https://github.com/VisionLearningGroup/R-C3D">github repository</a>.
</li>
<li> Our paper <a href="arxiv.org/abs/1707.04046"> Stable Distribution Alignment Using the Dual of the Adversarial Distance </a> was accpeted to WICLR 2018.
<li> Two papers accepted to ICCV 2017: <a href="https://arxiv.org/abs/1704.05526"> Learning to Reason (End-to-end Neural Modules) </a>
and <a href="https://arxiv.org/abs/1703.07814"> R-C3D for action detection in video</a>.
</li>
<li>
Our <a href="http://www.ulrichviereck.com/CoRL2017">paper on robots learning to grasp</a> is accepted to CoRL 2017. <br>
<iframe width="320" height="180" src="https://www.youtube.com/embed/ZGzlLc9bg1A" frameborder="0" allowfullscreen></iframe> <br>
</li>
<li>
<p>The <a href="http://ai.bu.edu/visda-2017/">VisDA-2017 Challenge winners</a> have been announced on October 10.
Join us at the <a href="http://adas.cvc.uab.es/task-cv2017/"> TASK-CV workshop </a> to find out more!</p>
</li>
<li> Slides from Kate's talk at the <a href="https://sites.google.com/site/nips2016intelligenttrans/schedule">NIPS 2016 workshop on Machine Learning for Intelligent
Transportation Systems</a> on December 9,
<a href="https://drive.google.com/file/d/0B4IapRTv9pJ1czJkQUpMSWpLSVU/view">Domain Adaptation for Perception and Action.</a>
</li>
<li> We are hosting the <a href="http://vision.cs.uml.edu/necv2016.html"> 2016 New England Computer Vision Workshop (NECV)</a> at Boston University.
</li>
<li> Our paper titled Deep CORAL: Correlation Alignment for Deep Domain Adaptation has won the <a href="/pubs/awardTASCK-CV2016.pdf"> <font color="red">Honorable Mention Paper</font></a> of the TASK-CV workshop at ECCV'2016.
</li>
<li>Our paper
<a href="http://arxiv.org/abs/1511.05234">"Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering"</a>
is accepted to ECCV 2016. Here is the video spotlight:<br><br>
<iframe width="320" height="180" src="https://www.youtube.com/embed/FjpRwVKYJQ8?rel=0" frameborder="0" allowfullscreen></iframe>
<br><br>
</li>
<li>Our paper <a href="https://arxiv.org/abs/1609.04356">Combining Texture and Shape Cues for Object Detection with
Minimal Supervision</a> was accepted to ACCV-16.
</li>
<li>Our paper
<a href="https://arxiv.org/abs/1605.06695">"Fine-to-coarse Knowledge Transfer For Low-Res Image Classification"</a>
was accepted to ICIP-16.
</li>
<li> <font color="red">Moving to Boston University!</font> Kate has accepted an Assistant Professor position in the
<a href="http://www.bu.edu/cs/">Computer Science Department</a> at <a href="http://www.bu.edu/">Boston University</a>, and will be moving this summer, along with her group. Stay tuned for the new website.
</li>
<li> <a href="https://arxiv.org/abs/1607.01719">Deep CORAL: Correlation Alignment for Deep Domain Adaptation (Extended Abstract)</a>.
</li>
<li> Two orals accepted to <a href="http://cvpr2016.thecvf.com/">CVPR 2016</a>:
<a href="http://arxiv.org/abs/1511.05284">Deep Compositional Captioning: Describing Novel Object Categories
without Paired Training Data</a> and <a href="http://arxiv.org/abs/1511.04164">Natural Language Object Retrieval</a>.
</li>
<li> Slides from Kate's MIT talk on March 15th:
<a href="https://drive.google.com/file/d/0B4IapRTv9pJ1eWowSzJBNGh0aFU/view?usp=sharing">Adaptive Deep Learning for
Vision and Language.</a>
</li>
<li> Our paper titled Return of Frustratingly Easy Domain Adaptation (Extended Abstract) has won the <a href="/pubs/awardTASCK-CV2015-Best.pdf"> <font color="red">Best Paper Prize</font></a> of the TASK-CV workshop at ICCV'2015.
</li>
<li> Our paper titled <a href="http://arxiv.org/abs/1511.05547">Return of Frustratingly Easy Domain Adaptation</a> was accepted to AAAI-16.
</li>
<li> Our group is co-organizing the <a href="https://sites.google.com/site/tlworkshop2015/">Transfer and Multi-Task Learning: Trends and New Perspectives Workshop</a> at NIPS 2015 on December 12th, 2015 in Montreal, Canada.
</li>
<li> Four papers accepted to ICCV 2015! Here are some spotlights:<br><br>
<iframe width="320" height="180" src="https://www.youtube.com/embed/-xNI7e7YgDk?rel=0" frameborder="0" allowfullscreen></iframe>
<iframe width="320" height="180" src="https://www.youtube.com/embed/gQMDX1Q2OfY?rel=0" frameborder="0" allowfullscreen></iframe>
<br><br>
</li>
<li> Slides from Kate's lecture at the <a href="">Microsoft Machine Learning and Intelligence School</a>, which took place in St Petersburg, Russia, are available here:
<a href="https://drive.google.com/file/d/0B4IapRTv9pJ1V1N1LUFmUVRDbWc/view?usp=sharing">part1</a>, <a href="https://drive.google.com/file/d/0B4IapRTv9pJ1MmlSc0NtSXdmMmc/view?usp=sharing">part2</a>.
</li>
<li> We will present <a href="pubs/cvpr2015_workshop_virtual_dataset.pdf">Generating Large Scale Datasets from 3D CAD Models</a> at the <a href="https://sites.google.com/site/cvpr2015futureofdataworkshop/schedule">workshop</a>, initial datasets are available <a href="http://vision.cs.uml.edu/datasets/">here</a>
</li>
<li> Kate is co-organizing the <a href="https://sites.google.com/site/cvpr2015futureofdataworkshop/schedule">Future of Datasets in Vision Workshop</a> at CVPR 2015 on June 11th, 2015 in Boston, MA.
</li>
<li> <a href="https://drive.google.com/open?id=0B4IapRTv9pJ1b0xObHVsMmxieHc">Slides</a> from my recent <a href="http://opendatascicon.com/schedule/deep-learning/">tutorial</a> on the deep learning library <a href="caffe.berkeleyvision.edu">Caffe</a> at the <a href="http://opendatascicon.com/">Open Data Science Conference</a> on May 30th in Boston.
</li>
<li> <a href="http://lsda.berkeleyvision.org">Large-Scale Detection by Adaptation</a> 7K Category Detection models are now available!
</li>
<li> Our paper titled <a href="/pubs/bmvc14_sun_fromvirtualtoreal.pdf">From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains</a> was accepted to BMVC 2014. See also <a href="bmvc14_FromVirtualToReality.pptx">slides</a> from a recent talk.
</li>
<li>
<a href="./projects.html#data">DeCAF features</a> that achieve the state of the art on the Office domain adaptation dataset are now available for download.
</li>
<li> Kate is co-chairing the <a href="http://www.cvc.uab.es/adas/task-cv2014">TASK-CV Workshop on Transferring and Adapting Source Knowledge in Computer Vision</a>, co-located with ECCV2014.</li>
<li> Kate will be giving a <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.360.3572&rep=rep1&type=pdf">tutorial on Domain Transfer Learning for Vision Applications</a> at
<a href="http://tab.computer.org/pamitc/archive/cvpr2012/program-details/tutorials.html"> CVPR 2012</a> with Dong Xu and Ivor Tsang.</li>
<li> Kate is co-organizing the <a href="http://nips.cc/Conferences/2011/Program/event.php?ID=2539"> Workshop on Integrating Language and Vision</span></a>, held at NIPS 2011 in Grenada, Spain.</li>
</ul>
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