[FFmpeg-devel] Regarding GSoC 2020 project proposal

[Pedro Arthur]

Hi,

Em qua., 4 de mar. de 2020 às 09:57, YATENDRA SINGH
<yatendras at iitbhilai.ac.in> escreveu:
>
> Thank you for explaining the procedure.
> I have posted my own project proposal on the page you had instructed me to.
> Looking forward to the feedback.


Have you contacted any possible mentor? If not, I would suggest you to
make your project idea less generic.
For example: which dnn models are you planning to use (both for the
qualification and the project)?
Are they already supported by our dnn infrastructure? our dnn module
has two backends: native, which is cpu only and tensorflow. Which one
are you going to support? or both?

>
>
> Regards,
> Yatendra Singh.
>
> On Tue, Mar 3, 2020 at 10:19 PM Pedro Arthur <bygrandao at gmail.com> wrote:
>
> > Hi
> >
> > Em ter., 3 de mar. de 2020 às 09:24, YATENDRA SINGH
> > <yatendras at iitbhilai.ac.in> escreveu:
> > >
> > > Hi,
> > > I am a third year CSE student at the Indian Institute of Technology
> > Bhilai,
> > > and would like to contribute to ffmpeg this year. I have
> > > relevant experience with Machine Learning and would like to work on
> > > improving the video frame interpolation already implemented. With such a
> > > plethora of great Machine Learning Algorithms being published every year
> > at
> > > prestigious conferences I would aim to read the relevant academic papers
> > > and implement the best suited technique for the task. For example, Depth
> > > Aware Video Frame Interpolation (DAIN CVPR-2019) is supposedly the state
> > of
> > > the art method on Vimeo90k and MiddleBury
> > > <https://paperswithcode.com/task/video-frame-interpolation> but at the
> > same
> > > time Frame Interpolation with Generative Adversarial Network(FIGAN), uses
> > > not CNN but multi-scale synthesis( MS ) to get higher speeds.
> > > Looking forward to hearing from you soon.
> > >
> > > Yatendra SIngh
> > > Frame Interpolation with Multi-Scale Deep Loss Functions and Generative
> > > Adversarial NetworksFrame Interpolation with Multi-Scale Deep Loss
> > > Functions and Generative Adversarial NetworksFrame Interpolation with
> > > Multi-Scale Deep Loss Functions and Generative Adversarial Networks
> >
> > I suppose this project is your own idea as it is not listed in the
> > projects page, right?
> >
> > I think it would be good to add you idea under "Your Own Project Idea"
> > section in [1] adding as much information as possible so that we can
> > evaluate your idea and possible assign a mentor / backup mentor.
> > A few things I think are important to evaluate your project are:
> > *have a well defined "expected result", will it be a filter? or
> > something else? we already have a dnn module and a dnn_processing
> > filter, will your project be using it?
> >
> > *what is the amount of work that will be done during the project, more
> > or less this is related to above "expected result"
> >
> > *define a qualification task, we can discuss it after the above is define
> >
> > *sell your idea (not strictly necessary but may help evaluating your
> > project), why is it useful feature to have, what improvements it
> > brings, etc
> >
> > [1] -
> > https://trac.ffmpeg.org/wiki/SponsoringPrograms/GSoC/2020#YourOwnProjectIdea
> > _______________________________________________
> > ffmpeg-devel mailing list
> > ffmpeg-devel at ffmpeg.org
> > https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
> >
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> > ffmpeg-devel-request at ffmpeg.org with subject "unsubscribe".
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