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Abstract:
Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. First, we need to give computers insight into our world, and our daily lives. Not just through the charade the we present to the world on social media, but through a genuine look at the most boring, mundane, routine aspects of our lives.

In this thesis, we explored techniques for crowdsourcing the creation of this data from hundreds of people in their own homes, and analyzed how humans think about activities along with the best strategies for annotating complex data of this nature. Given this insight into human behavior, we can start understanding where other vision techniques have trouble, understand how to improve them, and which venues are most promising moving forward. We hope this kind of realistic bias may provide new insights that aid robots equipped with our computer vision models operating in the real world.

In this talk, we will survey these contributions, and highlight our recent work using pairs of first-person and third-person videos, and using unsupervised web videos to learn concepts for unsupervised translation.

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Thesis Committee Members:
Abhinav Gupta, Chair
Martial Hebert
Deva Ramanan
Cordelia Schmid, Inria
Ivan Laptev, Inria