State-of-the-art writing in machine learning According to Wikipedia, State-of-the-art is as follows


  State-of-the-art (sometimes cutting edge or leading edge) refers to
  the highest level of general development, as of a device, technique,
  or scientific field achieved at a particular time. However, in some
  contexts it can also refer to a level of development reached at any
  particular time as a result of the common methodologies employed at
  the time.


So there are two interpretations for state-of-art: one is general development across time and another one is the  best method available today.

Which interpretation is apt for state-of-the-art writing in machine learning? 

For example consider a task Semantic Segmentation on PASCAL VOC 2012 test. There are 34 methods to perform that. Among them DeepLabv3+ (Xception-JFT)
has only the SOTA status. Remember that remaining 33 methods might also have SOTA status at their times.

If I need to write and present State-of-the-art on Semantic Segmentation on PASCAL VOC 2012 test, then is it enough to present DeepLabv3+  and ignore remaining (assume no dependencies) or there is a need to understand remaining also for state-of-the -art?

Context: My University generally follows the interpretation I for state-of-the-art, but popular machine learning website paperswithcode is following interpretation II. So I got confuse and hence asking which one is more apt for state-of-the-art writing. If interpretation II is the actual one,  then there may be possibility for me to convince my guide and act accordingly.
State of the art refers only to the very highest accuracy ever achieved up to the point that you write your paper in computer science (I work in the field).