I tend to disagree on this. A method that improves on a previous best in class benchmark by ~30 percent is certainly impressive. While supervised learning state of the art benchmarks for visual tracking are significantly higher, we shouldn't ignore unsupervised (or essentially unsupervised, as in this case) methods simply because they are currently weaker. While they may remain weaker, there's immense value in increasing our unsupervised benchmarks, as another commenter pointed out.