Mugs: A Multi-Granular Self-Supervised Learning Framework

TL;DR

Mugs introduces a novel self-supervised learning framework that explicitly captures multi-granular visual features, improving performance across diverse downstream tasks by combining instance, local-group, and group discriminations.

Contribution

The paper proposes the first multi-granular self-supervised learning framework, Mugs, which integrates three complementary granular supervisions to learn diverse feature levels.

Findings

Achieves 82.1% linear probing accuracy on ImageNet-1K, surpassing previous SOTA by 1.1%.

Outperforms existing methods on transfer learning, detection, and segmentation tasks.

Effectively captures fine- and coarse-grained features for versatile downstream applications.

Abstract

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.