  In self-supervised learning, multi-granular features are heavily desired
though rarely investigated, as different downstream tasks (e.g., general and
fine-grained classification) often require different or multi-granular
features, e.g.~fine- or coarse-grained one or their mixture. In this work, for
the first time, we propose an effective MUlti-Granular Self-supervised learning
(Mugs) framework to explicitly learn multi-granular visual features. Mugs has
three complementary granular supervisions: 1) an instance discrimination
supervision (IDS), 2) a novel local-group discrimination supervision (LGDS),
and 3) a group discrimination supervision (GDS). IDS distinguishes different
instances to learn instance-level fine-grained features. LGDS aggregates
features of an image and its neighbors into a local-group feature, and pulls
local-group features from different crops of the same image together and push
them away for others. It provides complementary instance supervision to IDS via
an extra alignment on local neighbors, and scatters different local-groups
separately to increase discriminability. Accordingly, it helps learn high-level
fine-grained features at a local-group level. Finally, to prevent similar
local-groups from being scattered randomly or far away, GDS brings similar
samples close and thus pulls similar local-groups together, capturing
coarse-grained features at a (semantic) group level. Consequently, Mugs can
capture three granular features that often enjoy higher generality on diverse
downstream tasks over single-granular features, e.g.~instance-level
fine-grained features in contrastive learning. By only pretraining on
ImageNet-1K, Mugs sets new SoTA linear probing accuracy 82.1$\%$ on ImageNet-1K
and improves previous SoTA by $1.1\%$. It also surpasses SoTAs on other tasks,
e.g. transfer learning, detection and segmentation.
