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The content of this blog is my personal opinion only. Although I am an employee - currently of Imagination Technologies's MIPS group, in the past of other companies such as Intellectual Ventures, Intel, AMD, Motorola, and Gould - I reveal this only so that the reader may account for any possible bias I may have towards my employer's products. The statements I make here in no way represent my employer's position, nor am I authorized to speak on behalf of my employer. In fact, this posting may not even represent my personal opinion, since occasionally I play devil's advocate.

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Wednesday, August 16, 2017

Does Vision Research Drive Deep Learning Startups? | Chris Rowen | Pulse | LinkedIn

I think that I would like this conference format.  In preference to a multitrack conference where there are multiple papers I want to watch in the same timeslot.



Half of the reason to attend a conference in person is to observe the audience questions.



I really like the idea of poster sessions for all presented papers.





Does Vision Research Drive Deep Learning Startups? | Chris Rowen | Pulse | LinkedIn:

So many teams submit worthwhile papers [to CVPR]  that it has adopted a format to expose as many people to as many papers as possible. Roughly 2500 papers were submitted this year, of which only about 30% are accepted. Even so how can people absorb more than 750 papers? All the accepted papers get into poster sessions, and those sessions are unlike any poster session I’ve seen before. You often find two or three authors surrounded by a crowd of 25 or more, each explaining the gist of the talk over and over again for all comers. Some notable papers are given a chance to be shared a rapid-fire FOUR minute summary in one of many parallel tracks. And even smaller handful of papers gets much bigger exposure – a whole TWELVE minute slot, including Q&A!   
Remarkably the format does work – the short talks serve as useful teasers to draw people to the posters. A session of short talks gives a useful cross section of the key application problems and algorithmic methods across the whole field of computer vision. That broad sampling of the papers confirms the near-total domination of computer vision by deep learning methods. 


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