Back before deep learning, there were a lot of different attempts at computer vision. Some involved Conditional Random Fields and Markov Random Fields, which were both computationally difficult and hard to understand/implement.
Are these areas still being developed in the computer vision domain? What was the end result of this line of study? I haven't seen any papers on this topic be cited in top-performing benchmarks, so I assume nobody cares about them anymore, but I wanted to ask.