f9us98a

It gets the best results for what? Maybe for classification and recognition.

But the point of the article is that there is more to computer vision. Stereo, optical flow, geometry, and physics can only be aided by deep learning so much.

Another point not mentioned is the computational power required for deep learning. Consider programming the physics for a ball rolling down an incline. You could use (1) the math itself, vs. (2) a neural net. It's clear that the direct math approach could be orders of magnitude faster than neural nets. I wouldn't be surprised if directly coding the physics would achieve 1,000x the performance of a neural net.

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.