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Carl Vondrick
@cvondrick
Associate Professor at @Columbia.
New York, NY
Joined October 2012
Posts
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    Our latest work shows that learning to colorize videos causes visual tracking to emerge automatically! Blog: ai.googleblog.com/2018/06/self-s… Paper: arxiv.org/abs/1806.09594 @alirezafathi @kevskibombom @sguada @abhi2610
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    Fantastic conditional GAN results by Isola et al phillipi.github.io/pix2pix/
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    The future is hard to anticipate! In our latest #CVPR2021 paper, we introduce a framework for learning *what* is predictable in the future. Rather than committing up front to categories to predict, our approach learns how to hedge the bet. hyperfuture.cs.columbia.edu
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    Finding Tiny Faces -- had to zoom in quite a bit to parse how cool the results are! arxiv.org/pdf/1612.04402…
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    Learning unsupervised machine translation is easier if you open your eyes! Image distributions create transitive relations between languages. This creates incidental supervision for learning multilingual representations on 50 unpaired languages arxiv.org/pdf/2012.04631… @Surisdi
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    Learning Features by Watching Objects Move by Pathak et al arxiv.org/pdf/1612.06370…
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    amazing generations of the video future! Red border means output, green is input. sites.google.com/a/umich.edu/ru…
    GIF
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    SoundNet: Learning natural sound representations with convnets and 2 million unlabeled videos. web.mit.edu/vondrick/sound…
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    Recognizing objects and scenes from sound only. Turn on your speakers! More visualizations: projects.csail.mit.edu/soundnet/
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    Unsupervised Learning by Predicting Noise by Bojanowski and Joulin. Cool yet simple idea that works quite well!! arxiv.org/pdf/1704.05310…
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    What causes adversarial examples? Latest #ECCV2020 paper from @ChengzhiM and Amogh shows that deep networks are vulnerable partly because they are trained on too few tasks. Just by increasing tasks, we strengthen robustness for each task individually. arxiv.org/pdf/2007.07236…
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    Oops! Dave+Bo introduce a dataset of unconstrained videos showing unintentional action. We study self-supervised approaches for learning video representations of intentionality. #CVPR2020 Poster 93, Tue 10am PST Website: oops.cs.columbia.edu Paper: arxiv.org/abs/1911.11206
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    Learning from Unlabeled Video (#LUV❤️‍🔥) starts today at 1:50pm EDT / 10:50am PDT! sites.google.com/view/luv2021 You will LUV the speaker lineup and the curated papers! 😍 featuring @pathak2206 @akanazawa @SongShuran and more #CVPR2021 #CVPR21 #CVPR