Computer vision and partial solutions

In computer vision, no problem has been completely solved. People tend to move to more complex problems in an anxiety to do something new, but the fact is that this fatally builds upon a very wobbly foundation of partial solutions. My fear is that there is a high risk that nothing really gets done in this process. Here is an excerpt from a researcher in my field:

Monkey Collects Corn Cobs.
Before an old and more basic problem is completely solved, one would accept a partial, suboptimal solution and use it in a solution to a new or supposedly more challenging problem. When a partial solution or result is found, one moves on again. Such a constant shift of focus is very counterproductive. When others try to revisit some of the problems, one would claim that the problems have already been addressed and there would be no need for any improvement. It is impossible to measure progress or compare different solutions if they are all to some extent partial. From a pedagogical perspective, from partial results like that, it is impossible to develop a systematic body of knowledge that can be effectively transferred to future students and researchers. -- Yi. Ma, in Warning Signs of Bugus Progress in Research in an Age of Rich Computation and Information, 2007.

That is something to think about. Nowadays, researchers have the big challenge of dealing with vast informational and computational resources. All this power also leads to lack of focus and fundamental research when used without discipline.