DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency

Understand

In this paper, we propose a simple yet effective transformer framework for self-supervised learning called DenseDINO to learn dense visual representations.

• To exploit the spatial information that the dense prediction tasks require but neglected by the existing self-supervised transformers, we introduce point-level supervision across views in a novel token-based way.
• Specifically, DenseDINO introduces some extra input tokens called reference tokens to match the point-level features with the position prior.
• With the reference token, the model could maintain spatial consistency and deal with multi-object complex scene images, thus generalizing better on dense prediction tasks.