[CMU AI Seminar] Mar 9 (Zoom) -- Kayvon Fatahalian (Stanford) -- Keeping the Domain Expert in the Loop: Ideas to Models in Hours, Not Weeks -- AI Seminar sponsored by Fortive

[Shaojie Bai]

Hi all,

Just a reminder that the CMU AI Seminar <http://www.cs.cmu.edu/~aiseminar/> is
tomorrow 12pm-1pm:
https://cmu.zoom.us/j/92196990617?pwd=Zk1vZkhzbTkwTE1nNzcyMm5JTFRpUT09.

*Kayvon Fatahalian* (Stanford University) will be talking about how we
could come up with great model ideas and training workflows on
images/videos quickly in practice (see below).

Thanks,
Shaojie

On Tue, Mar 2, 2021 at 1:10 PM Shaojie Bai <shaojieb at andrew.cmu.edu> wrote:

> Dear all,
>
> We look forward to seeing you *next Tuesday (3/9)* from 12:00-1:00 PM
> (U.S. Eastern time) for the next talk of our *CMU AI seminar*, sponsored
> by Fortive <https://careers.fortive.com/>.
>
> To learn more about the seminar series or see the future schedule, please
> visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
> <http://www.cs.cmu.edu/~aiseminar/>
>
> On 3/9, *Kayvon Fatahalian <http://graphics.stanford.edu/~kayvonf/>* (Stanford
> University) will be giving a talk on "*Keeping the Domain Expert in the
> Loop: Ideas to Models in Hours, Not Weeks*."
>
> *Title*: Keeping the Domain Expert in the Loop: Ideas to Models in Hours,
> Not Weeks
>
> *Talk Abstract*: My students and I often find ourselves as "subject
> matter experts" needing to create models for use in big data computer
> graphics and video analysis applications. Yet it is frustrating that a
> capable grad student, armed with a large unlabeled image/video collection,
> a palette of modern pre-trained models, and an idea of what novel object or
> event they want to detect, still requires days-to-weeks to create good
> models for their task. In this talk, I will discuss challenges we've faced
> carrying out the iterative process of data curation, model training, and
> model validation for the specific case of rare events and categories in
> image and video collections (such as professional broadcast sports and
> cable TV). Our ultimate goal (not yet achieved) is to create training
> techniques and data selection interfaces that enable interactive,
> grad-student-in-the-loop workflows where the expert human is working
> concurrently with massive amounts of parallel processing to interactively
> and continuously perform cycles of data acquisition, training, and
> validation.
>
> *Speaker Bio*: Kayvon Fatahalian is an Assistant Professor in the
> Computer Science Department at Stanford University. His lab works on visual
> computing systems projects, including high-performance rendering for RL,
> large-scale video analytics, programming systems for video data mining, and
> compilation techniques for optimizing image processing pipelines. In all
> these efforts, the goal is to enable rapid development of applications that
> involve image and video processing at scale.
>
> *Zoom Link*:
> https://cmu.zoom.us/j/92196990617?pwd=Zk1vZkhzbTkwTE1nNzcyMm5JTFRpUT09
>
>
> Thanks,
> Shaojie Bai (MLD)
>
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