> Other than a handful number of people doing some fundamental research towards understanding the theoretical concepts of these methods, almost all the community now seems to target the development of more complex pipelines (that most likely cannot be reproduced based on the elements presented in the paper) which in most of the cases have almost no theoretical reasoning behind that can add 0,1% of performance on a given benchmark. Is this the objective of academic research? Putting in place highly complex engineering models that simply explore computing power and massive annotated data?
I last dabbled in image processing research around 2011. Probably most of the papers i read during the previous 5 years were small little epsilon papers that added no real value. I did some work in other fields and noticed a similar trend there. I always attributed it to the trend of PhDs being pumped through the system in ever greater numbers and the need for researchers to publish a paper every few months.
This happened to me. I developed a computer vision technique that achieved a well known result but without the same constraints. Although it never surpassed the other technique for most images, it worked in a wide variety of cases that the previous technique did not.
My major professor diluted the paper and added other content consistent with the previous method. Not just adding prior art to the introduction, but changing the meat of the paper so that it didn't seem like a departure.
He assured me that this would make it easier to publish, and publishing was all that mattered. There were no bonus points for publishing a novel technique, and there would certainly be extra work having to deal with referees.
I'm very glad to be out of that environment now. I noped out of academia and happily dealing with corporate B.S.
Having dropped out of University while studying Computer Vision, i've witnessed this much too often in former friends and colleagues.
Smart people doing remarkable things don't seem to have a place in our society anymore, neither in academia nor in the private economy. Sure there are exceptions.
Microsoft Research, Google X and of course a handful of universities that actually work as they are supposed to, but for most of my ex colleague's these weren't real options as noone ever instilled the courage in them to find their way there, or survive the competition.
It's strange that most of them are building CRUD Software, Writing Shaders for Game Engines or work in marketing, instead of pushing us towards breakthroughs in CV and with that AI.
I keep having that same thought. However, making a breakthrough requires 2 things:
1. Resources. Time and money. These research ventures might take years and sometimes need some funding in addition to your own salary. It is basically borderline impossible for most developers to contribute anything useful in an environment where they need to worry about paying their bills for this month.
2. Know-how. Right now, most industries have advanced so much that you need very specialized knowledge and a metric fuckton of math/stats to contribute to any particular field in any significant way. A developer with a BS in CS can most often not even understand the papers being currently published due to the high math/specialized required knowledge.
> Smart people doing remarkable things don't seem to have a place in our society anymore, neither in academia nor in the private economy. Sure there are exceptions.
I think smart people doing remarkable things have greater visibility than they have at any point in the recent past due to the internet and platforms like YouTube, etc. It is easier than ever to share and discover knowledge now than it ever has been in the past..
The biggest problem I see is that there is limited compensation for participating in these endeavors. I think this is an issue from the standpoint that some research is very costly or requires resources that the average person cannot obtain. There is still a great number of discoveries that can be made by people working in labs they have made wherever they found space.
In addition to that, not every publicly funded school provide these publicly funded software packages freely for public use, hoping by keeping them private to exploit their "business value" somehow. At least in this part of Europe.
Deep learning may have become the flavor of the season now, but ironically its pioneers like LeCun and Hinton too faced similar problems when they tried to publish [1].
> He assured me that this would make it easier to publish, and publishing was all that mattered. There were no bonus points for publishing a novel technique, and there would certainly be extra work having to deal with referees.
He was a terrible professor! Yes, publish or perish is real, but that's such a terrible, pessimistic attitude to have. Its like he had given up on trying to actually do research. Tell him to go become an adjunct.
My professor during my Master's adventure was insistent on publishing a lot. He didn't mind if it was little deltas. He burned the phrase "publish or perish" into my brain. But had I figured out something novel, he would have absolutely supported putting it out there. That would have been the whole point of doing this work! Deltas help you survive, but novel ideas are what you should strive for.
> Probably most of the papers i read during the previous 5 years were small little epsilon papers that added no real value. I did some work in other fields and noticed a similar trend there. I always attributed it to the trend of PhDs being pumped through the system
No, the reason for this is that all the easy stuff has already been discovered. 20 years ago it was still kind of easy for a single PhD student to make enormous progress in his field, but after a dozen of PhD generstions there is just not much left which can be discovered by a si gle student.
Just look at physics, they had to build a multi-billion dollar research facility below the ground to advance our knowledge. Or the satelites for discovering gravity waves, ... All of this can not be done in the classic PhD model where a single PhD student works on a topic.
tldr: After 150+ years of science there is almost nothing left which can be discovered by a single PhD student
> tldr: After 150+ years of science there is almost nothing left which can be discovered by a single PhD student
With 150+ years of science lots of new kinds of questions emerged that hardly anyone went deeply into. Just begin working there and make deep contributions. To make a few things clear:
* You probably won't get any academic recognition for this or probably no research funding agency will be willing to fund your research (far too experimental).
* It is quite possible that despite you being really talented your research into this will come to nothing. That is a prize one has to pay for the possibility of doing a deep contribution.
If you are looking for ideas where to start, just look around yourself and from what you see try to create a deep general theory (the more abstract and general the better IMHO) which has lots of predictive power and either allows you to formalize a theory where you can prove theorems about (my personally prefered way, since I'm mathematician) or has strong falsifiable predictions that one can in principle do experiments on.
This. Most of the comments on this thread support your statements - people don't vary because of the risks, the biggest one being not getting published.
I'm an advocate of publishing not just the paper, but the compilable source and executable binary code and datasets used for just that reason. Science is not science if others can't reproduce the results and tweak and permute the inputs, reliably and repeatedly, IMHO.
I feel like someone should start a group or meetup for people who are interested in novel, valuable techniques, rather than whatever will advance their scientific career. It could be filled by hobbyists, including retired people who could spend a lot of time on it.
I last dabbled in image processing research around 2011. Probably most of the papers i read during the previous 5 years were small little epsilon papers that added no real value. I did some work in other fields and noticed a similar trend there. I always attributed it to the trend of PhDs being pumped through the system in ever greater numbers and the need for researchers to publish a paper every few months.