  Self-supervised research improved greatly over the past half decade, with
much of the growth being driven by objectives that are hard to quantitatively
compare. These techniques include colorization, cyclical consistency, and
noise-contrastive estimation from image patches. Consequently, the field has
settled on a handful of measurements that depend on linear probes to adjudicate
which approaches are the best. Our first contribution is to show that this test
is insufficient and that models which perform poorly (strongly) on linear
classification can perform strongly (weakly) on more involved tasks like
temporal activity localization. Our second contribution is to analyze the
capabilities of five different representations. And our third contribution is a
much needed new dataset for temporal activity localization.
