Whether an approach is currently better than other well understood approaches shouldn't be the focus of researchers. It might be the focus of private sphere engineers, but researchers should be interested in whether a new approach has potential to advance machine learning.
I don't mean to suggest that papers should boil down to performance comparisons with baseline results, not at all.
What I'm saying is that if you don't do this comparison somewhere, it can be very hard to tell what your numeric results do mean. In the worse case you see people offering numerical comparisons to other approaches that are similarly unpinned, and you can't tell if they are interesting even if they are apples to apples.
As a researcher, you are being lax if you never do that work if for nothing else than a sanity check on your implementation. If you've done it, it's good to include in the paper as a point of reference. If you currently aren't performing better than baseline, that's fine - but you should understand why and discuss that with insight too.
So it isn't the focus. But it is table stakes that you understand this stuff.
