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

• URL: http://arxiv.org/abs/2306.04654v1
• Date: Tue, 6 Jun 2023 15:04:45 GMT
• Title: DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency
• Authors: Yike Yuan, Xinghe Fu, Yunlong Yu, Xi Li
• Abstract summary: We propose a transformer framework for self-supervised learning called DenseDINO to learn dense visual representations. Specifically, DenseDINO introduces some extra input tokens called reference tokens to match the point-level features with the position prior. Compared with the vanilla DINO, our approach obtains competitive performance when evaluated on classification in ImageNet.
• Score: 12.881617910150688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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