[Lisa_seminaires] Timnit Gebru (Stanford CV Lab, PhD candidate), May 5, 13:45, AA5340

Junyoung Chung elecegg at gmail.com
Ven 5 Mai 13:10:49 EDT 2017


This talk will start in 30 minutes.

On Sun, Apr 30, 2017 at 9:50 PM Junyoung Chung <elecegg at gmail.com> wrote:

> Hi all,
>
> the next tea-talk will be given by Timnit Gebru from Stanford university.
> The room will be AA5340, and the talk will start at 13:45.
> Here is the detail of the talk.
>
> *Title: *Using Deep Learning and Google Street View to Estimate the
> Demographic Makeup of the US
>
> *Abstract: *Targeted socio-economic policies require an accurate
> understanding of a country’s demographic makeup. To that end, the United
> States spends more than 1 billion dollars a year gathering census data such
> as race, gender, education, occupation and unemployment rates. Compared to
> the traditional method of collecting surveys across many years which is
> costly and labor intensive, data-driven, machine learning driven approaches
> are cheaper and faster—with the potential ability to detect trends in close
> to real time. In this work, we leverage the ubiquity of Google Street View
> images and develop a computer vision pipeline to predict income, per capita
> carbon emission, crime rates and other city attributes from a single source
> of publicly available visual data. We first detect cars in 50 million
> images across 200 of the largest US cities and train a model to determine
> demographic attributes using the detected cars. To facilitate our work, we
> used a graph-based algorithm to collect the largest and most challenging
> fine-grained dataset reported to date consisting of over 2600 classes of
> cars comprised of images from Google Street View and other web sources. Our
> prediction results correlate well with ground truth income (r=0.82), race,
> education, voting, sources investigating crime rates, income segregation,
> per capita carbon emission, and other market research. Finally, we learn
> interesting relationships between cars and neighborhoods allowing us to
> perform the first large-scale sociological analysis of cities using
> computer vision techniques.
>
> *Bio: *I am a PhD student in the Stanford Artificial Intelligence
> Laboratory, studying computer vision under Fei-Fei Li. My main research
> interest lies in data mining large-scale publicly available images to gain
> sociological insight, and working on computer vision problems that arise as
> a result. Some of these include fine-grained image recognition, scalable
> annotation of images, and domain adaptation. Prior to joining Fei-Fei's lab
> I worked at Apple designing circuits and signal processing algorithms for
> various Apple products including the first iPad. I also spent an obligatory
> year as an entrepreneur (as all Stanford undergrads seem to do). My
> research is supported by the NSF foundation GRFP fellowship and currently
> the Stanford DARE fellowship.
>
> Best,
> --Junyoung
>
-- 
--Junyoung
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