# DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency Source: [DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:23:24.960Z Authors: Yike Yuan, Xinghe Fu, Yunlong Yu, Xi Li Published: 2023-06-06T15:04:45.000Z ## Abstract 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. Compared with the vanilla DINO, our approach obtains competitive performance when evaluated on classification in ImageNet and achieves a large margin (+7.2% mIoU) improvement in semantic segmentation on PascalVOC under the linear probing protocol for segmentation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()